Velox: Learning Representations of 4D Geometry and Appearance

2026-05-08 · Source: Apple Machine Learning Research · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

Velox is a novel framework designed to learn latent representations of 4D objects, focusing on capturing geometry and appearance from unstructured dynamic point clouds. It trains an encoder to compress spatiotemporal color point clouds into dynamic shape tokens. These tokens are then used to supervise two decoders: a 4D surface decoder for time-varying surface distribution and a Gaussian decoder for learning appearance by mapping tokens to 3D Gaussians. The framework aims for descriptive, compressive, and accessible representations, requiring minimal input. Velox demonstrates strong performance across downstream tasks including video-to-4D generation, 3D tracking, and cloth simulation via image-to-4D generation.

Key takeaway

For research scientists developing 4D object representation models, Velox offers a robust framework for learning geometry and appearance from dynamic point clouds. You should consider its dual-decoder approach for surface distribution and Gaussian mapping to enhance representation quality and efficiency in applications like 4D generation and 3D tracking.

Key insights

Velox learns descriptive and compressive 4D object representations from dynamic point clouds using dual decoders.

Principles

• Compress spatiotemporal data into dynamic shape tokens.
• Use complementary decoders for geometry and appearance.

Method

Velox encodes dynamic point clouds into shape tokens, then decodes them via a 4D surface decoder for geometry and a Gaussian decoder for appearance.

In practice

• Generate 4D objects from video input.
• Improve 3D object tracking accuracy.
• Simulate cloth dynamics from images.

Topics

• 4D Geometry Representation
• Dynamic Point Clouds
• Latent Representations
• Spatiotemporal Learning
• 3D Gaussians

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Apple Machine Learning Research.