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Text-guided diffusion models have shown superior performance in image/video generation and editing. While few explorations have been performed in 3D scenarios. In this paper, we discuss three fundamental and interesting problems on this…

Computer Vision and Pattern Recognition · Computer Science 2023-10-13 Gang Li , Heliang Zheng , Chaoyue Wang , Chang Li , Changwen Zheng , Dacheng Tao

The emergence of text-driven motion synthesis technique provides animators with great potential to create efficiently. However, in most cases, textual expressions only contain general and qualitative motion descriptions, while lack fine…

Computer Vision and Pattern Recognition · Computer Science 2023-12-27 Dong Wei , Xiaoning Sun , Huaijiang Sun , Bin Li , Shengxiang Hu , Weiqing Li , Jianfeng Lu

Current approaches for 3D human motion synthesis generate high quality animations of digital humans performing a wide variety of actions and gestures. However, a notable technological gap exists in addressing the complex dynamics of multi…

Computer Vision and Pattern Recognition · Computer Science 2024-07-30 Anindita Ghosh , Rishabh Dabral , Vladislav Golyanik , Christian Theobalt , Philipp Slusallek

Diffusion models have shown great promise for image generation, beating GANs in terms of generation diversity, with comparable image quality. However, their application to 3D shapes has been limited to point or voxel representations that…

Computer Vision and Pattern Recognition · Computer Science 2022-12-16 Gimin Nam , Mariem Khlifi , Andrew Rodriguez , Alberto Tono , Linqi Zhou , Paul Guerrero

Text-driven image and video diffusion models have recently achieved unprecedented generation realism. While diffusion models have been successfully applied for image editing, very few works have done so for video editing. We present the…

Computer Vision and Pattern Recognition · Computer Science 2023-02-03 Eyal Molad , Eliahu Horwitz , Dani Valevski , Alex Rav Acha , Yossi Matias , Yael Pritch , Yaniv Leviathan , Yedid Hoshen

Diffusion models, such as Stable Diffusion, have shown incredible performance on text-to-image generation. Since text-to-image generation often requires models to generate visual concepts with fine-grained details and attributes specified…

Computer Vision and Pattern Recognition · Computer Science 2024-04-26 Xuehai He , Weixi Feng , Tsu-Jui Fu , Varun Jampani , Arjun Akula , Pradyumna Narayana , Sugato Basu , William Yang Wang , Xin Eric Wang

Extended reality (XR) demands generative models that respond to users' tracked real-world motion, yet current video world models accept only coarse control signals such as text or keyboard input, limiting their utility for embodied…

Computer Vision and Pattern Recognition · Computer Science 2026-02-23 Linxi Xie , Lisong C. Sun , Ashley Neall , Tong Wu , Shengqu Cai , Gordon Wetzstein

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…

Computer Vision and Pattern Recognition · Computer Science 2025-12-05 Yuduo Jin , Brandon Haworth

Generating animated 3D objects is at the heart of many applications, yet most advanced works are typically difficult to apply in practice because of their limited setup, their long runtime, or their limited quality. We introduce ActionMesh,…

Computer Vision and Pattern Recognition · Computer Science 2026-04-02 Remy Sabathier , David Novotny , Niloy J. Mitra , Tom Monnier

Learning a generalizable bimanual manipulation policy is extremely challenging for embodied agents due to the large action space and the need for coordinated arm movements. Existing approaches rely on Vision-Language-Action (VLA) models to…

Computer Vision and Pattern Recognition · Computer Science 2025-06-02 Chenyou Fan , Fangzheng Yan , Chenjia Bai , Jiepeng Wang , Chi Zhang , Zhen Wang , Xuelong Li

Denoising diffusion models have shown great promise in human motion synthesis conditioned on natural language descriptions. However, integrating spatial constraints, such as pre-defined motion trajectories and obstacles, remains a challenge…

Computer Vision and Pattern Recognition · Computer Science 2023-10-31 Korrawe Karunratanakul , Konpat Preechakul , Supasorn Suwajanakorn , Siyu Tang

We introduce the Multi-Motion Discrete Diffusion Models (M2D2M), a novel approach for human motion generation from textual descriptions of multiple actions, utilizing the strengths of discrete diffusion models. This approach adeptly…

Computer Vision and Pattern Recognition · Computer Science 2024-07-22 Seunggeun Chi , Hyung-gun Chi , Hengbo Ma , Nakul Agarwal , Faizan Siddiqui , Karthik Ramani , Kwonjoon Lee

Diffusion models have recently advanced photorealistic human synthesis, although practical talking-head generation (THG) remains constrained by high inference latency, temporal instability such as flicker and identity drift, and imperfect…

Computer Vision and Pattern Recognition · Computer Science 2026-03-09 Soumya Mazumdar , Vineet Kumar Rakesh

We present a novel image editing scenario termed Text-grounded Object Generation (TOG), defined as generating a new object in the real image spatially conditioned by textual descriptions. Existing diffusion models exhibit limitations of…

Computer Vision and Pattern Recognition · Computer Science 2024-03-18 Xiangtian Xue , Jiasong Wu , Youyong Kong , Lotfi Senhadji , Huazhong Shu

Humans frequently grasp, manipulate, and move objects. Interactive systems assist humans in these tasks, enabling applications in Embodied AI, human-robot interaction, and virtual reality. However, current methods in hand-object synthesis…

Robotics · Computer Science 2025-03-10 Sammy Christen

We address the task of generating temporally consistent and physically plausible images of actions and object state transformations. Given an input image and a text prompt describing the targeted transformation, our generated images…

Computer Vision and Pattern Recognition · Computer Science 2024-04-03 Tomáš Souček , Dima Damen , Michael Wray , Ivan Laptev , Josef Sivic

Recent advancements in diffusion models have significantly improved the realism and generalizability of character-driven animation, enabling the synthesis of high-quality motion from just a single RGB image and a set of driving poses.…

Computer Vision and Pattern Recognition · Computer Science 2025-12-02 Alireza Javanmardi , Pragati Jaiswal , Tewodros Amberbir Habtegebrial , Christen Millerdurai , Shaoxiang Wang , Alain Pagani , Didier Stricker

With the impressive progress in diffusion-based text-to-image generation, extending such powerful generative ability to text-to-video raises enormous attention. Existing methods either require large-scale text-video pairs and a large number…

Computer Vision and Pattern Recognition · Computer Science 2023-10-18 Ruiqi Wu , Liangyu Chen , Tong Yang , Chunle Guo , Chongyi Li , Xiangyu Zhang

We propose a novel learned deep prior of body motion for 3D hand shape synthesis and estimation in the domain of conversational gestures. Our model builds upon the insight that body motion and hand gestures are strongly correlated in…

Computer Vision and Pattern Recognition · Computer Science 2021-04-08 Evonne Ng , Shiry Ginosar , Trevor Darrell , Hanbyul Joo

While diffusion models have shown impressive performance in 2D image/video generation, diffusion-based Text-to-Multi-view-Video (T2MVid) generation remains underexplored. The new challenges posed by T2MVid generation lie in the lack of…

Computer Vision and Pattern Recognition · Computer Science 2024-06-14 Bing Li , Cheng Zheng , Wenxuan Zhu , Jinjie Mai , Biao Zhang , Peter Wonka , Bernard Ghanem