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Authors:Korrawe Karunratanakul, Konpat Preechakul, Emre Aksan, Thabo Beeler, Supasorn Suwajanakorn, Siyu 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 Karunratanakul, Konpat Preechakul, Supasorn Suwajanakorn, Siyu 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 Karunratanakul, Sergey Prokudin, Otmar Hilliges, Siyu 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 Karunratanakul, Adrian Spurr, Zicong Fan, Otmar Hilliges, Siyu Tang
We present HALO, a neural occupancy representation for articulated hands that produce implicit hand surfaces from input skeletons in a differentiable manner.
Grasping Field: Learning Implicit Representations for Human Grasps
Conference: International Virtual Conference on 3D Vision (3DV) 2020 oral presentation & best paper
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.