5 citations · 8 across the 13 of their papers we have counts for
13 papers
Large-scale Codec Avatars: The Unreasonable Effectiveness of Large-scale Avatar Pretraining
Junxuan Li, Rawal Khirodkar, Chengan He +37
High-quality 3D avatar modeling faces a critical trade-off between fidelity and generalization. On the one hand, multi-view studio data enables high-fidelity modeling of humans wit…
DuoMo: Dual Motion Diffusion for World-Space Human Reconstruction
Yufu Wang, Evonne Ng, Soyong Shin +8
We present DuoMo, a generative method that recovers human motion in world-space coordinates from unconstrained videos with noisy or incomplete observations. Reconstructing such mot…
PanoLAM: Large Avatar Model for Gaussian Full-Head Synthesis from One-shot Unposed Image
Peng Li, Yisheng He, Yingdong Hu +8
We present a feed-forward framework for static Gaussian full-head reconstruction and novel-view rendering from a single unposed image. Unlike previous work that relies on time-cons…
LHM++: An Efficient Large Human Reconstruction Model for Pose-free Images to 3D
Lingteng Qiu, Peihao Li, Heyuan Li +9
Reconstructing animatable 3D humans from casually captured images of articulated subjects without camera or pose information is highly practical but remains challenging due to view…
LAM: Large Avatar Model for One-shot Animatable Gaussian Head
Yisheng He, Xiaodong Gu, Xiaodan Ye +6
We present LAM, an innovative Large Avatar Model for animatable Gaussian head reconstruction from a single image. Unlike previous methods that require extensive training on capture…
AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction
Lingteng Qiu, Shenhao Zhu, Qi Zuo +9
Generating animatable human avatars from a single image is essential for various digital human modeling applications. Existing 3D reconstruction methods often struggle to capture f…
LaMP: Language-Motion Pretraining for Motion Generation, Retrieval, and Captioning
Zhe Li, Weihao Yuan, Yisheng He +7
Language plays a vital role in the realm of human motion. Existing methods have largely depended on CLIP text embeddings for motion generation, yet they fall short in effectively a…
MoGenTS: Motion Generation based on Spatial-Temporal Joint Modeling
Weihao Yuan, Weichao Shen, Yisheng He +5
Motion generation from discrete quantization offers many advantages over continuous regression, but at the cost of inevitable approximation errors. Previous methods usually quantiz…
Atlas Gaussians Diffusion for 3D Generation
Haitao Yang, Yuan Dong, Hanwen Jiang +3
Using the latent diffusion model has proven effective in developing novel 3D generation techniques. To harness the latent diffusion model, a key challenge is designing a high-fidel…
VideoMV: Consistent Multi-View Generation Based on Large Video Generative Model
Qi Zuo, Xiaodong Gu, Lingteng Qiu +8
Generating multi-view images based on text or single-image prompts is a critical capability for the creation of 3D content. Two fundamental questions on this topic are what data we…
An Optimization Framework to Enforce Multi-View Consistency for Texturing 3D Meshes
Zhengyi Zhao, Chen Song, Xiaodong Gu +6
A fundamental problem in the texturing of 3D meshes using pre-trained text-to-image models is to ensure multi-view consistency. State-of-the-art approaches typically use diffusion…
Ctrl-Room: Controllable Text-to-3D Room Meshes Generation with Layout Constraints
Chuan Fang, Yuan Dong, Kunming Luo +3
Text-driven 3D indoor scene generation is useful for gaming, the film industry, and AR/VR applications. However, existing methods cannot faithfully capture the room layout, nor do…
PanoContext-Former: Panoramic Total Scene Understanding with a Transformer
Yuan Dong, Chuan Fang, Liefeng Bo +2
Panoramic image enables deeper understanding and more holistic perception of surrounding environment, which can naturally encode enriched scene context information comp…