arXiv AI
PACE: Precise AI Cinematic Expression
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arXiv AI
Sep 18
PACE: Precise AI Cinematic Expression: A Typed Specification for Script-Grounded Previsualization and Geometric Conformance
PACE (Precise AI Cinematic Expression) is a typed specification that captures a film’s spatial plan—screenplay evidence, characters, props, locations, and camera actions—at script, scene, shot, or panel levels, with inheritance to lower levels. A compiler transforms this plan into prompts for diffusion models and a metrically accurate 3D scene, while a camera solver ensures the declared framing is achieved. Evaluation on the Automatic Drive screenplay and 204 external shots shows high positional accuracy and improved action capture when poses are declared, though transitions and motion remain future work.
arXiv AI
Jul 28
FilmBench: A Film-Grade Benchmark for Cinematic Video Generation
arXiv:2607. 24241v1 Announce Type: cross Abstract: Progress in video generation keeps narrowing the visual gap between AI-generated and professionally produced footage, yet most benchmarks still draw prompts from web sources or LLM templates and score them with untrained, generic multimodal models.
Hugging Face Trending Papers
Jul 27
FilmBench: A Film-Grade Benchmark for Cinematic Video Generation
Progress in video generation keeps narrowing the visual gap between AI-generated and professionally produced footage, yet most benchmarks still draw prompts from web sources or LLM templates and score them with untrained, generic multimodal models. More fundamentally, their evaluation taxonomies remain rudimentary (overall visual quality, coarse text alignment and temporal smoothness) rather than the professional Cinematic Language criteria by which films are actually made and judged, so they assess basic video plausibility rather than film-grade craft.
arXiv Computer Vision
Sep 28
Auteur: Language-Driven Cinematographic Framing for Human-Centric Video Generation
Auteur is a language‑driven method that generates human‑centric camera framing for generative video models. It treats shots as framings relative to an actor, encoding shot size, angle, and composition as functions of human pose and motion, and uses a domain‑specific language that converts to standard 6‑DoF camera parameters. A fine‑tuned multimodal large language model acts as a virtual director, mapping natural language descriptions and coarse human motion to sparse DSL keyframes that are interpolated into continuous camera trajectories for video generation.
arXiv Computer Vision
3d ago
World-Calibrated Proposal-to-Action Flow for Vision-Language-Action Models
arXiv:2610.02323v1 Announce Type: cross Abstract: Flow-based Vision-Language-Action (VLA) policies generate action chunks by transporting samples from a task-agnostic isotropic Gaussian source. As th...
arXiv Computer Vision
Sep 25
CinematicVQA: Benchmarking Film-Grammar Reasoning in Large Vision-Language Models
CinematicVQA is a new benchmark for evaluating large vision‑language models on film‑grammar reasoning. It introduces the Cinematic Scene Graph, a structured representation linking filming techniques to perceptual effects and narrative functions, and tests models on tasks beyond low‑level technique recognition. The study finds a semantic gap where models excel at describing visuals but struggle to identify underlying techniques, and shows that fine‑tuning improves performance on narrative function and multi‑hop reasoning.