Hasil untuk "Motion pictures"

Davood Soleymanzadeh, Xiao Liang, Minghui Zheng

Open-loop end-to-end neural motion planners have recently been proposed to improve motion planning for robotic manipulators. These methods enable planning directly from sensor observations without relying on a privileged collision checker during planning. However, many existing methods generate only a single path for a given workspace across different runs, and do not leverage their open-loop structure for inference-time optimization. To address this limitation, we introduce Flow Motion Policy, an open-loop, end-to-end neural motion planner for robotic manipulators that leverages the stochastic generative formulation of flow matching methods to capture the inherent multi-modality of planning datasets. By modeling a distribution over feasible paths, Flow Motion Policy enables efficient inference-time best-of-$N$ sampling. The method generates multiple end-to-end candidate paths, evaluates their collision status after planning, and executes the first collision-free solution. We benchmark the Flow Motion Policy against representative sampling-based and neural motion planning methods. Evaluation results demonstrate that Flow Motion Policy improves planning success and efficiency, highlighting the effectiveness of stochastic generative policies for end-to-end motion planning and inference-time optimization. Experimental evaluation videos are available via this \href{https://zh.engr.tamu.edu/wp-content/uploads/sites/310/2026/03/FMP-Website.mp4}{link}.

Hongyuan Chen, Xingyu Chen, Youjia Zhang et al.

We present Motion 3-to-4, a feed-forward framework for synthesising high-quality 4D dynamic objects from a single monocular video and an optional 3D reference mesh. While recent advances have significantly improved 2D, video, and 3D content generation, 4D synthesis remains difficult due to limited training data and the inherent ambiguity of recovering geometry and motion from a monocular viewpoint. Motion 3-to-4 addresses these challenges by decomposing 4D synthesis into static 3D shape generation and motion reconstruction. Using a canonical reference mesh, our model learns a compact motion latent representation and predicts per-frame vertex trajectories to recover complete, temporally coherent geometry. A scalable frame-wise transformer further enables robustness to varying sequence lengths. Evaluations on both standard benchmarks and a new dataset with accurate ground-truth geometry show that Motion 3-to-4 delivers superior fidelity and spatial consistency compared to prior work. Project page is available at https://motion3-to-4.github.io/.

Yuduo Jin, Brandon Haworth

Diffusion models have emerged as a widely utilized and successful methodology in human motion synthesis. Task-oriented diffusion models have significantly advanced action-to-motion, text-to-motion, and audio-to-motion applications. In this paper, we investigate fundamental questions regarding motion representations and loss functions in a controlled study, and we enumerate the impacts of various decisions in the workflow of the generative motion diffusion model. To answer these questions, we conduct empirical studies based on a proxy motion diffusion model (MDM). We apply v loss as the prediction objective on MDM (vMDM), where v is the weighted sum of motion data and noise. We aim to enhance the understanding of latent data distributions and provide a foundation for improving the state of conditional motion diffusion models. First, we evaluate the six common motion representations in the literature and compare their performance in terms of quality and diversity metrics. Second, we compare the training time under various configurations to shed light on how to speed up the training process of motion diffusion models. Finally, we also conduct evaluation analysis on a large motion dataset. The results of our experiments indicate clear performance differences across motion representations in diverse datasets. Our results also demonstrate the impacts of distinct configurations on model training and suggest the importance and effectiveness of these decisions on the outcomes of motion diffusion models.

Thomas Ressler-Antal, Frank Fundel, Malek Ben Alaya et al.

Recent advances in text-to-video (T2V) and image-to-video (I2V) models, have enabled the creation of visually compelling and dynamic videos from simple textual descriptions or initial frames. However, these models often fail to provide an explicit representation of motion separate from content, limiting their applicability for content creators. To address this gap, we propose DisMo, a novel paradigm for learning abstract motion representations directly from raw video data via an image-space reconstruction objective. Our representation is generic and independent of static information such as appearance, object identity, or pose. This enables open-world motion transfer, allowing motion to be transferred across semantically unrelated entities without requiring object correspondences, even between vastly different categories. Unlike prior methods, which trade off motion fidelity and prompt adherence, are overfitting to source structure or drifting from the described action, our approach disentangles motion semantics from appearance, enabling accurate transfer and faithful conditioning. Furthermore, our motion representation can be combined with any existing video generator via lightweight adapters, allowing us to effortlessly benefit from future advancements in video models. We demonstrate the effectiveness of our method through a diverse set of motion transfer tasks. Finally, we show that the learned representations are well-suited for downstream motion understanding tasks, consistently outperforming state-of-the-art video representation models such as V-JEPA in zero-shot action classification on benchmarks including Something-Something v2 and Jester. Project page: https://compvis.github.io/DisMo

Greg Hainge

Analysing Jonathan Glazer’s Under the Skin (2013), this article provides an overview of the importation of suture theory from psychoanalysis into film theory and Žižek’s revisiting of this theory, then bringing about a rapprochement between the concepts of suture and Malabou’s destructive plasticity, as expounded in her work The New Wounded. The forms of wounded subjectivity we find there are unable to stitch themselves into the illusory narratives needed to enable them to access a fixed sense of individual or shared identity and enclose them in a subjectivity separated off from all else around. Here, on the contrary, alterity, the alien, is situated within. It is such a form of subjectivity, I argue, that we find in Glazer’s Under the Skin where this narrative plays out not only diegetically, as we witness the alien that has sutured itself inside a human envelope attempt and fail to articulate itself to an external narrative that remains inaccessible to it, but infratextually also. Indeed, ultimately this article suggests that Under the Skin can be read as a commentary on the viewing subject required by (and forms of subjectivity produced by) post-cinematic media forms that at first seem to operate according to a different logic to the cinematic syntax of classical cinema, but which may in fact require us to reconsider some of our assumptions with regard to all forms of cinematic subjectivity produced in the relations between spectator and screen.

Akishige Yuguchi, Tomoki Ishikura, Sung-Gwi Cho et al.

In this study, to perform the robotic stroke motions following the shape of the human back similar to the stroke motions by humans, in contrast to the conventional robotic stroke motion with a linear trajectory, we propose a trajectory generation method for a robotic stroke motion following the shape of the human back. We confirmed that the accuracy of the method's trajectory was close to that of the actual stroking motion by a human. Furthermore, we conducted a subjective experiment to evaluate the psychological effects of the proposed stroke motion in contrast to those of the conventional stroke motion with a linear trajectory. The experimental results showed that the actual stroke motion following the shape of the human back tended to evoke more pleasant and active feelings than the conventional stroke motion.

Manuel Kansy, Jacek Naruniec, Christopher Schroers et al.

Recent years have seen a tremendous improvement in the quality of video generation and editing approaches. While several techniques focus on editing appearance, few address motion. Current approaches using text, trajectories, or bounding boxes are limited to simple motions, so we specify motions with a single motion reference video instead. We further propose to use a pre-trained image-to-video model rather than a text-to-video model. This approach allows us to preserve the exact appearance and position of a target object or scene and helps disentangle appearance from motion. Our method, called motion-textual inversion, leverages our observation that image-to-video models extract appearance mainly from the (latent) image input, while the text/image embedding injected via cross-attention predominantly controls motion. We thus represent motion using text/image embedding tokens. By operating on an inflated motion-text embedding containing multiple text/image embedding tokens per frame, we achieve a high temporal motion granularity. Once optimized on the motion reference video, this embedding can be applied to various target images to generate videos with semantically similar motions. Our approach does not require spatial alignment between the motion reference video and target image, generalizes across various domains, and can be applied to various tasks such as full-body and face reenactment, as well as controlling the motion of inanimate objects and the camera. We empirically demonstrate the effectiveness of our method in the semantic video motion transfer task, significantly outperforming existing methods in this context. Project website: https://mkansy.github.io/reenact-anything/

Maria Ida Bernabei

A long-standing link exists between avant-garde and scientific cinema. In the 1920s, in fact, the former contributed to the construction of the latter: on the one hand, by its systematic inclusion in film clubs’ and film societies’ screening programs; on the other hand, by catalyzing the theoretical debate on the medium specificity because of the specific techniques it develops. Through the texts by philosophers, film makers and theorists of the time (Walter Benjamin, Germaine Dulac, Jean Epstein, Émile Vuillermoz, László Moholy-Nagy among others), this essay examines the role of microscope films in the construction of 1920s film theory, discussing several tropes and key concepts such as pure cinema, cinégraphie integrale, rhythm theory and optical “unconscious”.

Martin P. Rossouw

This response article addresses the conception of “film as philosophy” developed by Shawn Loht in his book Phenomenology of Film: A Heideggerian Account of the Film Experience (2017), with specific attention to the relevance and implications of Loht's approach for the broader debate beyond a strictly Heideggerian film-philosophy. The article proceeds in three distinct takes. The first take examines Loht's later-Heideggerian inspirations, arguing that although these more fundamental notions of philosophy open significant possibilities for film as philosophy, they nevertheless run the risk of being too embracive, as well as too elusive, to make a distinctive contribution to the debate. For its second take, the article resets its initial point of departure by considering how the earlier Heidegger of Being and Time cues in Loht's approach a special – albeit, ultimately, untenable – investment in cinematic reflexivity, with the stylistic hallmarks of Terrence Malick serving as a test case. In its final take, the article concludes by venturing a possible solution to the questions raised in the first two takes. Here it is proposed that, in contrast to Loht's general interest in cinematographic devices, the voiceover holds a far more sustainable promise of reflexivity, while at the same time giving more prominence to the viewer's acts of listening – which is what a quintessentially Heideggerian film phenomenology surely asks for.

Makito Kobayashi, Chen-Chieh Liao, Keito Inoue et al.

In this work, we proposed a new style-diverse dataset for the domain of motion style transfer. The motion dataset uses an industrial-standard human bone structure and thus is industry-ready to be plugged into 3D characters for many projects. We claim the challenges in motion style transfer and encourage future work in this domain by releasing the proposed motion dataset both to the public and the market. We conduct a comprehensive study on motion style transfer in the experiment using the state-of-the-art method, and the results show the proposed dataset's validity for the motion style transfer task.

Takeru Oba, Norimichi Ukita

Image-based motion prediction is one of the essential techniques for robot manipulation. Among the various prediction models, we focus on diffusion models because they have achieved state-of-the-art performance in various applications. In image-based motion prediction, diffusion models stochastically predict contextually appropriate motion by gradually denoising random Gaussian noise based on the image context. While diffusion models are able to predict various motions by changing the random noise, they sometimes fail to predict a contextually appropriate motion based on the image because the random noise is sampled independently of the image context. To solve this problem, we propose R2-Diff. In R2-Diff, a motion retrieved from a dataset based on image similarity is fed into a diffusion model instead of random noise. Then, the retrieved motion is refined through the denoising process of the diffusion model. Since the retrieved motion is almost appropriate to the context, it becomes easier to predict contextually appropriate motion. However, traditional diffusion models are not optimized to refine the retrieved motion. Therefore, we propose the method of tuning the hyperparameters based on the distance of the nearest neighbor motion among the dataset to optimize the diffusion model for refinement. Furthermore, we propose an image-based retrieval method to retrieve the nearest neighbor motion in inference. Our proposed retrieval efficiently computes the similarity based on the image features along the motion trajectory. We demonstrate that R2-Diff accurately predicts appropriate motions and achieves high task success rates compared to recent state-of-the-art models in robot manipulation.

Jiazhen Pan, Wenqi Huang, Daniel Rueckert et al.

In cardiac CINE, motion-compensated MR reconstruction (MCMR) is an effective approach to address highly undersampled acquisitions by incorporating motion information between frames. In this work, we propose a novel perspective for addressing the MCMR problem and a more integrated and efficient solution to the MCMR field. Contrary to state-of-the-art (SOTA) MCMR methods which break the original problem into two sub-optimization problems, i.e. motion estimation and reconstruction, we formulate this problem as a single entity with one single optimization. Our approach is unique in that the motion estimation is directly driven by the ultimate goal, reconstruction, but not by the canonical motion-warping loss (similarity measurement between motion-warped images and target images). We align the objectives of motion estimation and reconstruction, eliminating the drawbacks of artifacts-affected motion estimation and therefore error-propagated reconstruction. Further, we can deliver high-quality reconstruction and realistic motion without applying any regularization/smoothness loss terms, circumventing the non-trivial weighting factor tuning. We evaluate our method on two datasets: 1) an in-house acquired 2D CINE dataset for the retrospective study and 2) the public OCMR cardiac dataset for the prospective study. The conducted experiments indicate that the proposed MCMR framework can deliver artifact-free motion estimation and high-quality MR images even for imaging accelerations up to 20x, outperforming SOTA non-MCMR and MCMR methods in both qualitative and quantitative evaluation across all experiments. The code is available at https://github.com/JZPeterPan/MCMR-Recon-Driven-Motion.

Jingqi Li, Jiaqi Gao, Yuzhen Zhang et al.

As a unique biometric that can be perceived at a distance, gait has broad applications in person authentication, social security, and so on. Existing gait recognition methods suffer from changes in viewpoint and clothing and barely consider extracting diverse motion features, a fundamental characteristic in gaits, from gait sequences. This paper proposes a novel motion modeling method to extract the discriminative and robust representation. Specifically, we first extract the motion features from the encoded motion sequences in the shallow layer. Then we continuously enhance the motion feature in deep layers. This motion modeling approach is independent of mainstream work in building network architectures. As a result, one can apply this motion modeling method to any backbone to improve gait recognition performance. In this paper, we combine motion modeling with one commonly used backbone~(GaitGL) as GaitGL-M to illustrate motion modeling. Extensive experimental results on two commonly-used cross-view gait datasets demonstrate the superior performance of GaitGL-M over existing state-of-the-art methods.

Steve Benford, Anders Sundnes Løvlie, Karin Ryding et al.

Museums are interested in designing emotional visitor experiences to complement traditional interpretations. HCI is interested in the relationship between Affective Computing and Affective Interaction. We describe Sensitive Pictures, an emotional visitor experience co-created with the Munch art museum. Visitors choose emotions, locate associated paintings in the museum, experience an emotional story while viewing them, and self-report their response. A subsequent interview with a portrayal of the artist employs computer vision to estimate emotional responses from facial expressions. Visitors are given a souvenir postcard visualizing their emotional data. A study of 132 members of the public (39 interviewed) illuminates key themes: designing emotional provocations; capturing emotional responses; engaging visitors with their data; a tendency for them to align their views with the system's interpretation; and integrating these elements into emotional trajectories. We consider how Affective Computing can hold up a mirror to our emotions during Affective Interaction.

Michael Burke

In this article, I explore what I call the persecutory trope – which underscores the alterity of the phantom and its relentless haunting and spectral oppression of the protagonists – in recent American ghost films, connecting it to the ethical thought of the continental philosophers, Emmanuel Levinas and Jacques Derrida. Films like The Ring (Gore Verbinski, 2002), The Grudge (Takashi Shimizu, 2004), It Follows (Robert Mitchell, 2014), and Sinister (Scott Derrickson, 2012) depict terrifying spectral antagonists whose relentless persecution of the protagonists often defies comprehension and narrative closure. I suggest that these films comprise a specific supernatural subgenre due to the particular way in which their specters haunt the victims. The relentlessness of the spectral assailant, and the foreclosure of actions by which the specter is either expelled from or reintegrated into symbolic understanding of its victim, can be construed in terms of the ethical relationship between the other and the self in the work of Levinas and Derrida. Their focus on the moral agent's responsibility to an other, an obligation that the agent does not undertake voluntarily, entails the spectralization of ethical responsibility insofar as it does not rest on solid, evidential grounds. This article shows how the spectralization of the ethical resonates in recent American ghost films through the disruptive effects of the specter's haunting and responsive mourning enacted by protagonists.

Hans P. Moravec

Barmak Honarvar Shakibaei, Yifan Zhao, John Ahmet Erkoyuncu

The quality of fetal ultrasound images is significantly affected by motion blur while the imaging system requires low motion quality in order to capture accurate data. This can be achieved with a mathematical model of motion blur in time or frequency domain. We propose a new model of linear motion blur in both frequency and moment domain to analyse the invariant features of blur convolution for ultrasound images. Moreover, the model also helps to provide an estimation of motion parameters for blur length and angle. These outcomes might imply great potential of this invariant method in ultrasound imaging application.

James Harvey

F. Kerrigan

ABSTRACT The movie has been with us in a variety of forms for over a century. During that time the movie as an artefact has played a number of roles from pure entertainment to political propaganda to a way in which we preserve or pass down memories. The movie moves. Getting its name from the innovation of having moving pictures, with the first film showing a horse galloping as the camera recorded a series of stills in quick succession; the movie is about physical motion, but also about emotional provocation and films have always been implicated in the market, in creating market demand and marketing ideology. So, movies show moving pictures and they serve to move us emotionally. This paper reflects on the development of the movie as a storytelling device, the role that they play in our lives, and why the movies can be viewed as a marketplace icon.

Kirsten Moana Thompson

This paper explores the professional cross-connections between the Walt Disney studios, who pioneered the early adoption of Technicolor IV, DuPont, whose chemical research provided the colour pigments and Pyralin cels used in Disney’s films, and Faber Birren, one of the most influential corporate American consultants in colour design and marketing. It considers several aspects of colour that have not previously been considered in colour or animation studies: first, the material history of cellulose nitrate and acetate and its structural and aesthetic relationship to colour and transparency in animation; second, the role that colour paints and pigments developed by the DuPont company played as part of its targeting of Hollywood as a strategic new market, and third, the ways in which colour production was aesthetically informed by colour consultants like Birren across multiple realms, from cinema to interior design and architecture. By considering how colour production and design was situated within larger corporate strategies at DuPont in which colour was key to its industrial and consumer markets, I hope to enrich our understanding of the role that Disney, DuPont and Faber Birren played in the colour revolution of the mid-twentieth century.