Object functionalization is cast as neural graph completion over a functional graph of parts, contacts, and motions, followed by geometry realization that also rectifies erroneous motions, demonstrated on furniture with a new paired dataset.
arXiv preprint arXiv:2304.01186 , year=
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Presents a scene-adaptive 3D human animation method using ground-adaptive motion retargeting and viewpoint-adaptive latent fusion to control human trajectories and camera views, reporting gains on two benchmarks.
SignVerse-2M provides a 2-million-clip multilingual pose-native dataset for sign language derived from public videos via DWPose preprocessing to enable robust modeling in real-world conditions.
CameraCtrl enables accurate camera pose control in video diffusion models through a trained plug-and-play module and dataset choices emphasizing diverse camera trajectories with matching appearance.
Open-source text-to-video and image-to-video diffusion models generate high-quality 1024x576 videos, with the I2V variant claimed as the first to strictly preserve reference image content.
citing papers explorer
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Functionalization via Structure Completion and Motion Rectification
Object functionalization is cast as neural graph completion over a functional graph of parts, contacts, and motions, followed by geometry realization that also rectifies erroneous motions, demonstrated on furniture with a new paired dataset.
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3D Scene-Adaptive Trajectory-Controllable Human Image Animation with Camera Movement
Presents a scene-adaptive 3D human animation method using ground-adaptive motion retargeting and viewpoint-adaptive latent fusion to control human trajectories and camera views, reporting gains on two benchmarks.
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SignVerse-2M: A Two-Million-Clip Pose-Native Universe of 55+ Sign Languages
SignVerse-2M provides a 2-million-clip multilingual pose-native dataset for sign language derived from public videos via DWPose preprocessing to enable robust modeling in real-world conditions.
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CameraCtrl: Enabling Camera Control for Text-to-Video Generation
CameraCtrl enables accurate camera pose control in video diffusion models through a trained plug-and-play module and dataset choices emphasizing diverse camera trajectories with matching appearance.
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VideoCrafter1: Open Diffusion Models for High-Quality Video Generation
Open-source text-to-video and image-to-video diffusion models generate high-quality 1024x576 videos, with the I2V variant claimed as the first to strictly preserve reference image content.