16 citations · 16 across the 18 of their papers we have counts for
18 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…
Cinematic Compositing Using Character-Environment-Harmonized Video Generation Models
Tianyi Xiang, Mingming He, Li Ma +1
Cinematic compositing aims to integrate green-screen characters into novel environments while maintaining physical and photometric realism. Previous methods often fail to capture t…
GPUSimBench: Towards Scalable and Reliable GPU-Accelerated Simulators in Embodied AI
Huzhenyu Zhang, Shenghai Yuan, Wenrui Yan +4
Data-driven embodied AI is rapidly transitioning into a paradigm that scales training through massively parallel simulation, where GPU-accelerated simulators serve as the foundatio…
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…
BodyReLux: Temporally Consistent Full-Body Video Relighting
Li Ma, Mingming He, Xueming Yu +4
Being able to relight human performance is a fundamental task for post production and content creation. We present BodyReLux, a subject-specific video diffusion-based framework for…
SIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds
Yunsong Zhou, Hangxu Liu, Xuekun Jiang +12
Robotic manipulation with deformable objects represents a data-intensive regime in embodied learning, where shape, contact, and topology co-evolve in ways that far exceed the varia…
RetimeGS: Continuous-Time Reconstruction of 4D Gaussian Splatting
Xuezhen Wang, Li Ma, Yulin Shen +2
Temporal retiming, the ability to reconstruct and render dynamic scenes at arbitrary timestamps, is crucial for applications such as slow-motion playback, temporal editing, and pos…
Lighting in Motion: Spatiotemporal HDR Lighting Estimation
Christophe Bolduc, Julien Philip, Li Ma +3
We present Lighting in Motion (LiMo), a diffusion-based approach to spatiotemporal lighting estimation. LiMo targets both realistic high-frequency detail prediction and accurate il…
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…
FreSca: Scaling in Frequency Space Enhances Diffusion Models
Chao Huang, Susan Liang, Yunlong Tang +4
Latent diffusion models (LDMs) have achieved remarkable success in a variety of image tasks, yet achieving fine-grained, disentangled control over global structures versus fine det…
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…
Survey of Video Diffusion Models: Foundations, Implementations, and Applications
Yimu Wang, Xuye Liu, Wei Pang +4
Recent advances in diffusion models have revolutionized video generation, offering superior temporal consistency and visual quality compared to traditional generative adversarial n…
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…
Fitting Spherical Gaussians to Dynamic HDRI Sequences
Pascal Clausen, Li Ma, Mingming He +3
We present a technique for fitting high dynamic range illumination (HDRI) sequences using anisotropic spherical Gaussians (ASGs) while preserving temporal consistency in the compre…
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…
SpecNeRF: Gaussian Directional Encoding for Specular Reflections
Li Ma, Vasu Agrawal, Haithem Turki +5
Neural radiance fields have achieved remarkable performance in modeling the appearance of 3D scenes. However, existing approaches still struggle with the view-dependent appearance…