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Scalable sensor simulation is an important yet challenging open problem for safety-critical domains such as self-driving. Current works in image simulation either fail to be photorealistic or do not model the 3D environment and the dynamic…

Computer Vision and Pattern Recognition · Computer Science 2021-05-18 Yun Chen , Frieda Rong , Shivam Duggal , Shenlong Wang , Xinchen Yan , Sivabalan Manivasagam , Shangjie Xue , Ersin Yumer , Raquel Urtasun

Creating expressive character animations is labor-intensive, requiring intricate manual adjustment of animators across space and time. Previous works on controllable motion generation often rely on a predefined set of dense spatio-temporal…

Graphics · Computer Science 2025-07-28 Inwoo Hwang , Jinseok Bae , Donggeun Lim , Young Min Kim

Recent advances in foundational Video Diffusion Models (VDMs) have yielded significant progress. Yet, despite the remarkable visual quality of generated videos, reconstructing consistent 3D scenes from these outputs remains challenging, due…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Yisu Zhang , Chenjie Cao , Tengfei Wang , Xuhui Zuo , Junta Wu , Jianke Zhu , Chunchao Guo

Customized generation using diffusion models has made impressive progress in image generation, but remains unsatisfactory in the challenging video generation task, as it requires the controllability of both subjects and motions. To that…

Computer Vision and Pattern Recognition · Computer Science 2023-12-08 Yujie Wei , Shiwei Zhang , Zhiwu Qing , Hangjie Yuan , Zhiheng Liu , Yu Liu , Yingya Zhang , Jingren Zhou , Hongming Shan

Controllable character animation is an emerging task that generates character videos controlled by pose sequences from given character images. Although character consistency has made significant progress via reference UNet, another crucial…

Computer Vision and Pattern Recognition · Computer Science 2024-09-11 Jingkai Zhou , Benzhi Wang , Weihua Chen , Jingqi Bai , Dongyang Li , Aixi Zhang , Hao Xu , Mingyang Yang , Fan Wang

Camera control, which achieves diverse visual effects by changing camera position and pose, has attracted widespread attention. However, existing methods face challenges such as complex interaction and limited control capabilities. To…

Computer Vision and Pattern Recognition · Computer Science 2025-04-04 Xiaoda Yang , Jiayang Xu , Kaixuan Luan , Xinyu Zhan , Hongshun Qiu , Shijun Shi , Hao Li , Shuai Yang , Li Zhang , Checheng Yu , Cewu Lu , Lixin Yang

We propose a method for generating video-realistic animations of real humans under user control. In contrast to conventional human character rendering, we do not require the availability of a production-quality photo-realistic 3D model of…

Computer Vision and Pattern Recognition · Computer Science 2019-05-13 Lingjie Liu , Weipeng Xu , Michael Zollhoefer , Hyeongwoo Kim , Florian Bernard , Marc Habermann , Wenping Wang , Christian Theobalt

In this work, we introduce a novel approach for creating controllable dynamics in 3D-generated Gaussians using casually captured reference videos. Our method transfers the motion of objects from reference videos to a variety of generated 3D…

Computer Vision and Pattern Recognition · Computer Science 2024-07-09 Zhoujie Fu , Jiacheng Wei , Wenhao Shen , Chaoyue Song , Xiaofeng Yang , Fayao Liu , Xulei Yang , Guosheng Lin

Despite the recent progress in text-to-video generation, existing studies usually overlook the issue that only spatial contents but not temporal motions in synthesized videos are under the control of text. Towards such a challenge, this…

Computer Vision and Pattern Recognition · Computer Science 2023-12-06 Xi Chen , Zhiheng Liu , Mengting Chen , Yutong Feng , Yu Liu , Yujun Shen , Hengshuang Zhao

Effective and generalizable control in video generation remains a significant challenge. While many methods rely on ambiguous or task-specific signals, we argue that a fundamental disentanglement of "appearance" and "motion" provides a more…

Computer Vision and Pattern Recognition · Computer Science 2026-02-16 Mingzhi Sheng , Zekai Gu , Peng Li , Cheng Lin , Hao-Xiang Guo , Ying-Cong Chen , Yuan Liu

Large-scale video generative models, capable of creating realistic videos of diverse visual concepts, are strong candidates for general-purpose physical world simulators. However, their adherence to physical commonsense across real-world…

Computer Vision and Pattern Recognition · Computer Science 2025-03-11 Hritik Bansal , Clark Peng , Yonatan Bitton , Roman Goldenberg , Aditya Grover , Kai-Wei Chang

Camera-controlled video generation has achieved remarkable progress in recent years. However, existing video-to-video re-rendering methods primarily rely on Supervised Fine-Tuning using synthetic datasets. At present, there is an extreme…

Computer Vision and Pattern Recognition · Computer Science 2026-05-25 Zizun Li , Haoyu Guo , Runzhe Teng , Chunhua Shen , Tong He

Generative world models are increasingly used for video generation, where learned simulators are expected to capture the physical rules that govern real-world dynamics. However, evaluating whether generated videos actually follow these…

Computer Vision and Pattern Recognition · Computer Science 2026-05-12 Juyi Lin , Arash Akbari , Yumei He , Lin Zhao , Haichao Zhang , Arman Akbari , Xingchen Xu , Zoe Y. Lu , Enfu Nan , Hokin Deng , Edmund Yeh , Sarah Ostadabbas , Yun Fu , Jennifer Dy , Pu Zhao , Yanzhi Wang

Human video generation task has gained significant attention with the advancement of deep generative models. Generating realistic videos with human movements is challenging in nature, due to the intricacies of human body topology and…

Computer Vision and Pattern Recognition · Computer Science 2024-09-04 Zhangsihao Yang , Mengyi Shan , Mohammad Farazi , Wenhui Zhu , Yanxi Chen , Xuanzhao Dong , Yalin Wang

Predicting diverse object motions from a single static image remains challenging, as current video generation models often entangle object movement with camera motion and other scene changes. While recent methods can predict specific…

Computer Vision and Pattern Recognition · Computer Science 2024-12-03 Karran Pandey , Matheus Gadelha , Yannick Hold-Geoffroy , Karan Singh , Niloy J. Mitra , Paul Guerrero

Extracting human motion from large-scale web videos offers a scalable solution to the data scarcity issue in character animation. However, some human parts in many video frames cannot be seen due to off-screen captures or occlusions. It…

Computer Vision and Pattern Recognition · Computer Science 2025-12-16 Boyuan Li , Sipeng Zheng , Bin Cao , Ruihua Song , Zongqing Lu

A long-standing question in physical reasoning is whether video-based models need to rely on factorized representations of physical variables in order to make physically accurate predictions, or whether they can implicitly represent such…

Computer Vision and Pattern Recognition · Computer Science 2026-02-10 Sonia Joseph , Quentin Garrido , Randall Balestriero , Matthew Kowal , Thomas Fel , Shahab Bakhtiari , Blake Richards , Mike Rabbat

Recent diffusion-based text-to-image customization methods have achieved significant success in understanding concrete concepts to control generation processes, such as styles and shapes. However, few efforts dive into the realistic yet…

Computer Vision and Pattern Recognition · Computer Science 2025-12-03 Fan Wu , Cheng Chen , Zhoujie Fu , Jiacheng Wei , Yi Xu , Deheng Ye , Guosheng Lin

We present a novel method for generating geometrically realistic and consistent orbital videos from a single image of an object. Existing video generation works mostly rely on pixel-wise attention to enforce view consistency across frames.…

Computer Vision and Pattern Recognition · Computer Science 2026-04-15 Rong Wang , Ruyi Zha , Ziang Cheng , Jiayu Yang , Pulak Purkait , Hongdong Li

Controllability, temporal coherence, and detail synthesis remain the most critical challenges in video generation. In this paper, we focus on a commonly used yet underexplored cinematic technique known as Frame In and Frame Out.…

Computer Vision and Pattern Recognition · Computer Science 2025-10-27 Boyang Wang , Xuweiyi Chen , Matheus Gadelha , Zezhou Cheng
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