👥 Multi-Agent¶
🎞️ ECCV2026 · 3 paper notes
📌 Same area in other venues: 📷 CVPR2026 (2) · 🔬 ICLR2026 (47) · 💬 ACL2026 (40) · 🧪 ICML2026 (24) · 🤖 AAAI2026 (26) · 🧠 NeurIPS2025 (17)
- A Benchmark and Multi-Agent System for Instruction-driven Cinematic Video Compilation
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CineBench evaluates instruction-driven cinematic compilation, while the training-free CineAgents system analyzes source footage before planning and executing an edit, achieving a shot-level F1 of 64.13, temporal correctness of 52.09%, and adversarial rejection of 87.23% on this benchmark.
- AGE: Agentic Gaussian Editing in 3D Scenarios
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AGE coordinates existing 3D editing tools through planning, execution, reflection, and backtracking agents with shared spatial memory, achieving a CLIP text–image direction similarity of 0.192 versus DGE's 0.133 on a custom benchmark, although component ablations are needed to isolate the sources of improvement.
- Automatic Method Illustration Generation for AI Scientific Papers via Drawing Middleware Creation, Evolution, and Orchestration
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FigAgent turns recurring visual components into callable, evolvable drawing functions and orchestrates them through multi-agent lookahead search to produce editable SVGs, improving node-alignment F1 on FigAgentBench from PaperBanana's 46.3 to 51.4.