X-EraAI/ActPhysCause-Challenge resource

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1004 stars · 2 forks observed · 2026-09-03

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

ActPhysCause Challenge is a benchmark and dataset for action-conditioned physical and causal world modeling, hosted as Track 0 of the LoViF 2026 @ ECCV Workshop challenge. It provides 2,000 training episodes and 500 evaluation samples of dual-arm tabletop manipulation, where models must generate physically plausible future videos from a first frame and a natural-language task instruction.

Use cases

• generate future videos of robot manipulation from a first frame and instruction
• benchmark world models on physical plausibility and goal alignment
• train models on bimanual tabletop manipulation episodes
• evaluate action-conditioned video prediction for embodied AI
• compare submissions on a ranked leaderboard for future video generation
• study causal and physical understanding in video generation models

When to choose

• you need a standardized benchmark for action-conditioned future video generation
• you are entering the LoViF 2026 @ ECCV Workshop challenge
• you want training data for dual-arm tabletop manipulation video prediction
• you need to evaluate world models on physical plausibility and temporal coherence

When to avoid

• you need real-time robot control or policy execution rather than video prediction
• you need single-arm or non-tabletop manipulation scenarios
• you need evaluation annotations, which are hidden for the anonymized test set
• you need tracks beyond Track 0, which are reserved for future releases

Facets

dataset · maturity active

machine-learning video-processing benchmarking data-generation machine-learning computer-vision robotics artificial-intelligence simulation python cross-platform video-prediction world-modeling embodied-ai benchmark robot-manipulation bimanual-manipulation challenge eccv-workshop huggingface-dataset causal-reasoning gpu

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