# EgoX

> AI framework generating first-person egocentric videos from third-person video footage

EgoX is a research framework developed by KAIST AI and Seoul National University that synthesizes egocentric (first-person) video from a single exocentric (third-person) input. It lifts 2D footage into 3D point clouds to establish viewpoint priors, then refines the output using pretrained video diffusion models with custom geometry-guided attention. This allows researchers and creators to generate realistic POV shots, including hands and interaction details, from standard filmed scenes.

Website: https://keh0t0.github.io/EgoX

## What it is for

- Synthesize first-person camera perspectives from third-person movie clips or sports footage
- Generate egocentric training data for robotics and embodied AI agents using existing standard video
- Create immersive POV scenes from external camera angles for VFX and digital content creation

## Features

- Single exocentric video input processing
- 3D point cloud lifting to project egocentric spatial priors
- Width-wise and channel-wise latent concatenation to fuse viewpoints
- Geometry-guided self-attention to maintain spatial alignment and avoid visual artifacts
- Lightweight LoRA adaptation of pretrained video diffusion models

## Pricing

Open source

## Details

- Type: AI model
- Category: [AI](https://xiry.app/topics/ai.md)
- Topic: [AI Video Generation](https://xiry.app/topics/ai/ai-video-generation.md)
- Open source: yes
- Platforms: Linux, Self-hosted
- For: Computer vision researchers, embodied AI engineers, and visual effects artists
- Tags: video diffusion, egocentric, computer vision, point cloud, research paper

## Why it was saved

- [@evolving.ai, 22 December 2025](https://www.instagram.com/p/DSkPewZDdb6/): Allows creators and researchers to synthesize realistic first-person camera perspectives from existing third-person video clips.

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Updated 3 October 2026. Page: https://xiry.app/resources/egox

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