16 citations · 16 across the 10 of their papers we have counts for
10 papers
ID-V2V: Identity-Preserving Video Restylization
Yuancheng Xu, Mingming He, Pablo Salamanca +5
In visual storytelling, human performances are central to creative intent and narrative meaning. However, preserving human identity and performance while enabling flexible visual e…
Go-with-the-Track: Video Compositing and Motion Control with Point Tracking
Koichi Namekata, Yash Kant, Zhizheng Liu +9
Filmmaking demands precise motion control and reference image compositing -- capabilities that existing methods treat separately. Point-track-conditioned image-to-video models rest…
ReAge3D: Re-Aging 3D Faces with View Consistency
Libing Zeng, Li Ma, Mingming He +3
We present a novel framework for realistic and controllable 3D face re-aging which produces highly detailed, identity-preserving results. Existing 3D editing methods, while effecti…
DiffHDR: Re-Exposing LDR Videos with Video Diffusion Models
Zhengming Yu, Li Ma, Mingming He +11
Most digital videos are stored in 8-bit low dynamic range (LDR) formats, where much of the original high dynamic range (HDR) scene radiance is lost due to saturation and quantizati…
Detail Enhanced Gaussian Splatting for Large-Scale Volumetric Capture
Julien Philip, Li Ma, Pascal Clausen +8
We present a unique system for large-scale, multi-performer, high resolution 4D volumetric capture providing realistic free-viewpoint video up to and including 4K resolution facial…
Virtually Being: Customizing Camera-Controllable Video Diffusion Models with Multi-View Performance Captures
Yuancheng Xu, Wenqi Xian, Li Ma +10
We introduce a framework that enables both multi-view character consistency and 3D camera control in video diffusion models through a novel customization data pipeline. We train th…
Lux Post Facto: Learning Portrait Performance Relighting with Conditional Video Diffusion and a Hybrid Dataset
Yiqun Mei, Mingming He, Li Ma +9
Video portrait relighting remains challenging because the results need to be both photorealistic and temporally stable. This typically requires a strong model design that can captu…
Go-with-the-Flow: Motion-Controllable Video Diffusion Models Using Real-Time Warped Noise
Ryan Burgert, Yuancheng Xu, Wenqi Xian +10
Generative modeling aims to transform random noise into structured outputs. In this work, we enhance video diffusion models by allowing motion control via structured latent noise s…
Infinite-Resolution Integral Noise Warping for Diffusion Models
Yitong Deng, Winnie Lin, Lingxiao Li +5
Adapting pretrained image-based diffusion models to generate temporally consistent videos has become an impactful generative modeling research direction. Training-free noise-space…
DifFRelight: Diffusion-Based Facial Performance Relighting
Mingming He, Pascal Clausen, Ahmet Levent Taşel +8
We present a novel framework for free-viewpoint facial performance relighting using diffusion-based image-to-image translation. Leveraging a subject-specific dataset containing div…