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20232026
most citedOne Transformer Fits All Distributions in Multi-Modal Diffusion at Scale

29 citations · 30 across the 6 of their papers we have counts for

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cs.CV2026

Vorch-Human: Unified Multi-Task Human-Centric Generation via Long-Horizon Continuation

Yang Ding, Haoran Yu, Xin Ma +8

Human-centric audio-visual generation spans several closely related tasks: animating a person from driving speech, jointly generating speech and video from a voice reference, and s…

cs.CV2026

Vorch-Omni: Multi-Task Orchestration of Sight and Sound

Vorch Team, Xiaoyu Chen, Yang Ding +25

Recent advances in generative video modeling have enabled diverse generation, reference-based synthesis, extension, and editing, but existing approaches often rely on fragmented ta…

cs.CV2026

Vorch-Director: Interactive World Story Model via Noise-Aware Error Rectification

Lisai Zhang, Yidi Wu, Qi Liu +7

Autoregressive continuation provides a natural path toward minute-scale audio-visual generation by repeatedly extending a short-window generator conditioned on previously generated…

cs.CV2026

Vorch-IR: Long-Form Unified Multimodal Identity Replacement Video Generation

Yaole Wang, Xiaoyu Chen, Xin Ma +5

Video identity replacement seeks to transfer the identities of one or more subjects while preserving the motion, expressions, and temporal structure of a driving video. Existing me…

cs.CV2026

TIDE: Task-Isolated Diffusion for Unified Video Editing and Generation

Qi Liu, Gang Yue, Mingyu Yin +7

Recent advances in Diffusion Transformers have driven rapid progress in video generation and editing, yet these capabilities are still handled by separate, task-specific models. Bu…

cs.CV2024★ 1 cited

Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models

Fan Bao, Chendong Xiang, Gang Yue +7

We introduce Vidu, a high-performance text-to-video generator that is capable of producing 1080p videos up to 16 seconds in a single generation. Vidu is a diffusion model with U-Vi…