Korrawe Karunratanakul

PhD student
CAB G 89

Basic Information


Authors:Korrawe KarunratanakulKonpat PreechakulEmre AksanThabo BeelerSupasorn SuwajanakornSiyu Tang

Diffusion Noise Optimization (DNO) can leverage the existing human motion diffusion models as universal motion priors. We demonstrate its capability in the motion editing tasks where DNO can preserve the content of the original model and accommodates a diverse range of editing modes, including changing trajectory, pose, joint location, and avoiding newly added obstacles.

Authors:Korrawe KarunratanakulKonpat PreechakulSupasorn SuwajanakornSiyu Tang

Guided Motion Diffusion (GMD) model can synthesize realistic human motion according to a text prompt, a reference trajectory, and key locations, as well as avoiding hitting your toe on giant X-mark circles that someone dropped on the floor. No need to retrain diffusion models for each of these tasks!

Authors:Korrawe KarunratanakulSergey ProkudinOtmar HilligesSiyu Tang

We present HARP (HAnd Reconstruction and Personalization), a personalized hand avatar creation approach that takes a short monocular RGB video of a human hand as input and reconstructs a faithful hand avatar exhibiting a high-fidelity appearance and geometry.

Authors:Korrawe KarunratanakulAdrian SpurrZicong FanOtmar HilligesSiyu Tang

We present HALO, a neural occupancy representation for articulated hands that produce implicit hand surfaces from input skeletons in a differentiable manner.

Authors:Korrawe Karunratanakul, Jinlong Yang, Yan Zhang, Michael Black, Krikamol Muandet, Siyu Tang

Capturing and synthesizing hand-​object interaction is essential for understanding human behaviours, and is key to a number of applications including VR/AR, robotics and human-​computer interaction.