Yang Li

I'm a research scientist at Tencent XR vision Labs.

I finished my Ph.D. with Tatsuya Harada at The University of Tokyo. During my Ph.D., I did an internship at Technical University Munich with Matthias Nießner, and an internship with Bo Zheng at Huawei Japan Research Center. I did a master in bioinformatics with Tetsuo Shibuya at The University of Tokyo.

My research interests lie in the intersection of 3D computer vision, artificial intelligence, particularly focusing on registration, 3D/4D reconstruction, 3D AIGC, with applications in VR/AR, robotics, etc.

我们在上海有一些关于3D AIGC的研究实习生岗位，欢迎联系我们！

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Research

This paper proposes a novel approach for automatically generating interactive (i.e., playable) 3D game scenes from users' casual prompts, including hand-drawn sketches and text descriptions.

We present Frankenstein, a diffusion-based framework that can generate semantic-compositional 3D scenes in a single pass. Unlike existing methods that output a single, unified 3D shape, Frankenstein simultaneously generates multiple separated shapes, each corresponding to a semantically meaningful part.

We introduce the first 3D diffusion based approach for directly generating large unbounded 3D scene in both inddor and outdoor scenarios. At the core of this approach is a novel tri-plane diffusion and tri-plane extrapolation mechanism.

In this pa- per, we introduce a novel spatial-aware 3D shape generation framework that leverages 2D plane representations for enhanced 3D shape modeling.

We propose the first 2D-to-3D knowledge distillation strategy to enhance 3D semantic segmentation model with knowledge embedded in the latent space of powerful 2D models.

Neural Deformation Pyramid (NDP) break down non-rigid point cloud registration problem via hierarchical motion decomposition. NDP demonstrates advantage in both speed and registration accuracy.

We design Lepard, a novel partial point clouds matching method that exploits 3D positional knowledge. Lepard reaches SOTA on both rigid and deformable point cloud matching benchmarks.

We introduce 4DComplete, the first method that jointly recovers the shape and motion field from partial observations. We also provide a large-scale non-rigid 4D dataset for training and benchmaring. It consists of 1,972 animation sequences, and 122,365 frames.

SplitFusion is a dense RGB-D SLAM framework that simultaneously performs tracking and dense reconstruction for both rigid and non-rigid components of the scene.

We learn the tracking of non-rigid objects by differentiating through the underlying non-rigid solver. Specifically, we propose ConditionNet which learns to generate a problem-specific preconditioner using a large number of training samples from the Gauss-Newton update equation. The learned preconditioner increases PCG’s convergence speed by a significant margin.

We present a novel dense RGB-D SLAM solution that simultaneously accomplishes the dynamic/static segmentation and camera ego-motion estimation as well as the static background reconstructions.

We propose to leverage graph optimization and loop closure detection to overcome limitations of unsupervised learning based monocular visual odometry.

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