Lixin YANG (杨理欣)

Bio.  I’m a Research Assistant Professor in Shanghai Jiao Tong University (SJTU), School of Artificial Intelligence (SAI). Since 2019, I have been part of the Machine Vision and Intelligence Group under the supervision of Prof. Cewu Lu, where I obtained my Ph.D. in 2023. Prior to that, I received my M.S degree at the Intelligent Robot Lab in SJTU.
My research interests include 3D Vision and Robotics. Currently, I am focusing on modeling and imitating the hand manipulating objects, including 3D hand | object pose | shape estimation, grasp | motion generation, imitation learning, dexterous manipulation.

Join Us.  I am looking for 1) Master Student at SJTU School of Artificial Intelligence, 2) self-motivated undergraduate research interns. Contact me if you are interested in the above topics

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A two cascaded diffusion processes for object motion generation and robot action generation under object motion guidance.

A MLLM-based method that infuses language instructions into grasp generation; & A new language-pose dataset, CapGrasp, featuring detailed caption of grasping poses.

A 4D motion dataset focusing on bimanual object manipulation tasks involved in complex daily activities; & A three-tiered task abstraction: Object Affordance, Primitive Task, and Complex Task, to systematically organize manipulation tasks.

A full-body human motion dataset that captures text-guided desktop object rearrangement through MoCap and AR glasses; & A pipeline for generating avatar's motion of object rearrangement driven by text instruction.

A NeuS-based reconstruction method for both object surface and texture. Can be used to reconstruct object with complex geometry and texture given a hand-held object video.

A single-view hand-held object reconstruction method that exploits the categorical shape prior to reconstruct the shape of intra-class objects; & A new synthetic dataset, COMIC, that contains the category-level collection of objects with diverse shape, materials, interacting poses, and viewing directions.

A multi-view hand mesh recovery (HMR) method with Transformer. It leverages the "power of points", including Basis Points Set, point's positional encoding and point-Transformer, to unify and merge information from sparsely arranged cameras.

A MANO-derived hand model that contains 325 exquisite hand-crafted texture maps (vary in appearance and cover different kinds of blemishes, make-ups, and accessories).

A dataset that focuses on human grasp based on object's affordance. It contains two knowledge base: 1) Object affordance knowledge (Oak) and 2) Interaction knowledge (Ink).
A new model: Tink, for transferring interaction pose from one object to another.

An online data syhthesis tool for articulated hand(-object) pose estimation. An grasping systhesis method that can generate dexterous hand grasping poses for arbitrary object.

A novel contact representation (CPF) that used to imporve physical hand-object interaction. A hybrid learning-fitting framework (MIHO) that aligns the top-down pose estimation with bottom-up contact modeling.