Interpretable Motion-Attentive Maps: Spatio-Temporally Localizing Concepts in Video Diffusion Transformers
Interpretable Motion-Attentive Maps: Spatio-Temporally Localizing Concepts in Video Diffusion Transformers explores Develop a tool for generating interpretable maps to understand motion in video diffusion transformers.. Commercial viability score: 3/10 in Interpretable AI for Video.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Prior WorkFeeling the Space: Egomotion-Aware Video Representation for Efficient and Accurate 3D Scene UnderstandingViability: 3.0
- Higher ViabilityFrameDiT: Diffusion Transformer with Frame-Level Matrix Attention for Efficient Video GenerationViability: 7.0
- Higher ViabilityMotionGrounder: Grounded Multi-Object Motion Transfer via Diffusion TransformerViability: 8.0
- Higher ViabilityFine-grained Motion Retrieval via Joint-Angle Motion Images and Token-Patch Late InteractionViability: 6.0
- Higher ViabilityMotion-Adaptive Temporal Attention for Lightweight Video Generation with Stable Diffusion