1 citations · 1 across the 7 of their papers we have counts for
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Ring Forcing: Towards Precise Long-Term Memory for Autoregressive Video Diffusion
Bowen Xue, Brandon Y. Feng, Chenguo Lin +6
Scaling video generation to long durations reveals a critical bottleneck: current models lack robust long-term memory. This deficiency can be studied along two critical aspects: ob…
Latent-Frequency Validity: Fast Spectral Editing with Screened Video-VAE Transfer Operators
Bowen Xue, Jiafeng Xiong, Xin Quan
Direct spectral editing in video-VAE latents can control noise, flicker, smoothness, and frequency content without a decode--filter--reencode pass. However, video VAEs may redistri…
VideoNeuMat: Neural Material Extraction from Generative Video Models
Bowen Xue, Saeed Hadadan, Zheng Zeng +3
Creating photorealistic materials for 3D rendering requires exceptional artistic skill. Generative models for materials could help, but are currently limited by the lack of high-qu…
Stand-In: A Lightweight and Plug-and-Play Identity Control for Video Generation
Bowen Xue, Zheng-Peng Duan, Qixin Yan +6
Generating high-fidelity human videos that match user-specified identities is important yet challenging in the field of generative AI. Existing methods often rely on an excessive n…
Physics-Guided Motion Loss for Video Generation Model
Bowen Xue, Giuseppe Claudio Guarnera, Shuang Zhao +1
Current video diffusion models generate visually compelling content but often violate basic laws of physics, producing subtle artifacts like rubber-sheet deformations and inconsist…