Jionghao Wang

A humanoid policy steered by future goal images: a predictor turns subtask prompts into goal images, and a planner drives whole-body actions toward them across multi-task routines.

We learn dense rewards from action-free videos by modeling task-relevant latent dynamics, so the reward tracks task progress rather than visual distractors such as lighting.

PDT is a novel diffusion-based framework for transforming dense point distribution into a target distribution that is semantically meaningful.

In this paper, we introduced a novel text-to-avatar generation method that separately generates the human body and the clothes and allows high-quality animation on the generated avatar.

We proposed a novel diffusion-based pipeline that generates complete 360 panoramas using one or more unregistered NFoV images captured from arbitrary angles.

Developed QOED to enhance robot exploration by isolating identifiable model parameters and suppressing unidentifiable 'nuisance' directions in high-dimensional systems.

SPGen leverages Spherical Projection (SP) to generate high-quality 3D shapes with diffusion models.