1 citations · 1 across the 9 of their papers we have counts for
9 papers
HECTOR: Hybrid Editable Compositional Object References for Video Generation
Guofeng Zhang, Angtian Wang, Jacob Zhiyuan Fang +4
Real-world videos naturally portray complex interactions among distinct physical objects, effectively forming dynamic compositions of visual elements. However, most current video g…
StoryMem: Multi-shot Long Video Storytelling with Memory
Kaiwen Zhang, Liming Jiang, Angtian Wang +6
Visual storytelling requires generating multi-shot videos with cinematic quality and long-range consistency. Inspired by human memory, we propose StoryMem, a paradigm that reformul…
TGT: Text-Grounded Trajectories for Locally Controlled Video Generation
Guofeng Zhang, Angtian Wang, Jacob Zhiyuan Fang +8
Text-to-video generation has advanced rapidly in visual fidelity, whereas standard methods still have limited ability to control the subject composition of generated scenes. Prior…
ATI: Any Trajectory Instruction for Controllable Video Generation
Angtian Wang, Haibin Huang, Jacob Zhiyuan Fang +2
We propose a unified framework for motion control in video generation that seamlessly integrates camera movement, object-level translation, and fine-grained local motion using traj…
MAGREF: Masked Guidance for Any-Reference Video Generation with Subject Disentanglement
Yufan Deng, Yuanyang Yin, Xun Guo +8
We tackle the task of any-reference video generation, which aims to synthesize videos conditioned on arbitrary types and combinations of reference subjects, together with textual p…
CINEMA: Coherent Multi-Subject Video Generation via MLLM-Based Guidance
Yufan Deng, Xun Guo, Yizhi Wang +7
Video generation has witnessed remarkable progress with the advent of deep generative models, particularly diffusion models. While existing methods excel in generating high-quality…
FlexEControl: Flexible and Efficient Multimodal Control for Text-to-Image Generation
Xuehai He, Jian Zheng, Jacob Zhiyuan Fang +6
Controllable text-to-image (T2I) diffusion models generate images conditioned on both text prompts and semantic inputs of other modalities like edge maps. Nevertheless, current con…
E-ViLM: Efficient Video-Language Model via Masked Video Modeling with Semantic Vector-Quantized Tokenizer
Jacob Zhiyuan Fang, Skyler Zheng, Vasu Sharma +1
To build scalable models for challenging real-world tasks, it is important to learn from diverse, multi-modal data in various forms (e.g., videos, text, and images). Among the exis…
Text-to-image Editing by Image Information Removal
Zhongping Zhang, Jian Zheng, Jacob Zhiyuan Fang +1
Diffusion models have demonstrated impressive performance in text-guided image generation. Current methods that leverage the knowledge of these models for image editing either fine…