AI

4DCodeBench: Benchmarking Agents on Inverse Graphics of Dynamic Scenes

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Briefings

4DCodeBench introduces a benchmark for evaluating agents' ability to reconstruct dynamic scenes from video using code generation. The study curates real-world videos and synthetic scenes featuring deformation, fluid flow, and fracture to test frontier models. Key findings indicate that strong static reconstruction capabilities do not reliably translate to complex dynamics, highlighting a significant gap in current agent capabilities. The benchmark provides a testbed for tracking progress toward agents that can interpret the dynamics of the world through code.

Key points

• Agents must translate visual observations into compact representations of scene structure and dynamics by implementing abstractions such as physical simulations to reproduce complex behavior.
• Strong static reconstruction capabilities do not yet translate into reliable reconstruction of complex dynamics, indicating a significant gap in current agent capabilities.

Context and limitations

The benchmark is limited to video-based dynamic scene reconstruction and does not cover other modalities or static scene reconstruction tasks.

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