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TEACH: Temporal Action Compositions for 3D Humans Nikos Athanasiou · Mathis Petrovich · Michael J. Black · Gül Varol 3DV 2022
Generating shapes using natural language can enable new ways of imagining and creating the things around us. While significant recent progress has been made in text-to-image generation, text-to-shape generation remains a challenging problem due to the unavailability of paired text and shape data…
CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields
SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generation Nikos Athanasiou · Mathis Petrovich · Michael J. Black · Gül Varol ICCV 2023 Official PyTorch implementation of the paper "SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generation"
Official Pytorch implementation of the paper "MotionCLIP: Exposing Human Motion Generation to CLIP Space".
The main features of guytevet/motionclip are: 3D Generation, Motion Generation.
Projects with overlapping indexed features include: athn-nik/teach — TEACH: Temporal Action Compositions for 3D Humans Nikos Athanasiou · Mathis Petrovich · Michael J. Black · Gül… autodeskailab/clip-forge — Generating shapes using natural language can enable new ways of imagining and creating the things around us. While… cassiepython/clipnerf — CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields. guytevet/motion-diffusion-model — This is a PyTorch deep learning framework and tool for human motion synthesis that generates 3D character animations… hongfz16/avatarclip — AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars. athn-nik/sinc — SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generation Nikos Athanasiou · Mathis Petrovich ·…