EgoFlow: Gradient-Guided Flow Matching for Egocentric 6DoF Object Motion Generation
EgoFlow: Gradient-Guided Flow Matching for Egocentric 6DoF Object Motion Generation explores EgoFlow generates physically consistent 6DoF object trajectories from egocentric video by combining a Mamba-Transformer-Perceiver architecture with gradient-guided flow matching, outperforming existing methods in accuracy and realism.. Commercial viability score: 7/10 in Egocentric Motion Generation.
Updated 6 hours ago
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
- Builds On ThisFrom Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution DistillationViability: 6.0
- Prior WorkTIGFlow-GRPO: Trajectory Forecasting via Interaction-Aware Flow Matching and Reward-Driven OptimizationViability: 7.0
- Prior WorkAG-EgoPose: Leveraging Action-Guided Motion and Kinematic Joint Encoding for Egocentric 3D Pose EstimationViability: 7.0
- Prior WorkSafeFlow: Real-Time Text-Driven Humanoid Whole-Body Control via Physics-Guided Rectified Flow and Selective Safety GatingViability: 7.0