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AI models improve procedural planning and video generation

By PulseAugur Editorial · [2 sources] ·

Researchers have developed new methods for improving procedural planning and video generation by grounding them in instructional content and physical principles. One approach, RECIPE, uses reinforcement learning with a grounding quality reward to train models on large, noisy instructional video corpora, enhancing their ability to generate step-by-step plans. Another system, NEWTON, frames video generation as an agentic task, orchestrating various physics-aware tools and using a verifier for iterative re-planning to improve physical commonsense in generated videos. AI

IMPACT These methods could lead to more capable AI assistants that can understand and generate complex procedural tasks and physically realistic videos.

RANK_REASON Two research papers introducing novel methods for AI-driven procedural planning and video generation.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI models improve procedural planning and video generation

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Lorenzo Torresani ·

    RECIPE: Procedural Planning via Grounding in Instructional Video

    Visual planning asks a model to generate the remaining steps of a procedure in natural language given a partial video context and a goal. Progress on this task is bottlenecked by annotation: clean labeled datasets are small, domain-narrow, and encode a single execution trajectory…

  2. arXiv cs.CV TIER_1 English(EN) · Shujun Wang ·

    NEWTON: Agentic Planning for Physically Grounded Video Generation

    Video generation models produce visually compelling results but systematically violate physical commonsense -- on VideoPhy-2, the best model achieves only 32.6% joint accuracy. We identify a specification bottleneck: text prompts are lossy compression of the physical world, omitt…

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