EgoX: Egocentric Video Generation from a Single Exocentric Video
Egocentric perception enables humans to experience and understand the world directly from their own point of view. Translating exocentric (third-person) videos into egocentric (first-person) videos opens up new possibilities for immersive understanding but remains highly challenging due to extreme camera pose variations and minimal view overlap. This task requires faithfully preserving visible content while synthesizing unseen regions in a geometrically consistent manner. To achieve this, we present EgoX, a novel framework for generating egocentric videos from a single exocentric input. EgoX leverages the pretrained spatio temporal knowledge of large-scale video diffusion models through lightweight LoRA adaptation and introduces a unified conditioning strategy that combines exocentric and egocentric priors via width and channel wise concatenation. Additionally, a geometry-guided self-attention mechanism selectively attends to spatially relevant regions, ensuring geometric coherence and high visual fidelity. Our approach achieves coherent and realistic egocentric video generation while demonstrating strong scalability and robustness across unseen and in-the-wild videos.
693bc3359874a2a5e4ffb3e3
Taewoong Kang
claimed_verified
2025-12-12T09:14:18.431Z
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taewoongkang
Keh0t0
user
Keh0t0
693bc3359874a2a5e4ffb3e4
Kinam Kim
claimed_verified
2025-12-16T09:48:52.726Z
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Kinam Kim
kinam0252
user
kinam0252
693bc3359874a2a5e4ffb3e5
Dohyeon Kim
claimed_verified
2025-12-15T08:13:59.047Z
681afd78ba7048bcef707648
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Dohyeon Kim
kdh8156
user
kdh8156
693bc3359874a2a5e4ffb3e6
Minho Park
693bc3359874a2a5e4ffb3e7
Junha Hyung
693bc3359874a2a5e4ffb3e8
Jaegul Choo
EgoX: Egocentric Video Generation from a Single Exocentric Video
Egocentric perception enables humans to experience and understand the world directly from their own point of view. Translating exocentric (third-person) videos into egocentric (first-person) videos opens up new possibilities for immersive understanding but remains highly challenging due to extreme camera pose variations and minimal view overlap. This task requires faithfully preserving visible content while synthesizing unseen regions in a geometrically consistent manner. To achieve this, we present EgoX, a novel framework for generating egocentric videos from a single exocentric input. EgoX leverages the pretrained spatio temporal knowledge of large-scale video diffusion models through lightweight LoRA adaptation and introduces a unified conditioning strategy that combines exocentric and egocentric priors via width and channel wise concatenation. Additionally, a geometry-guided self-attention mechanism selectively attends to spatially relevant regions, ensuring geometric coherence and high visual fidelity. Our approach achieves coherent and realistic egocentric video generation while demonstrating strong scalability and robustness across unseen and in-the-wild videos.
EgoX framework generates egocentric videos from exocentric inputs using video diffusion models with LoRA adaptation, unified conditioning, and geometry-guided self-attention for coherence and visual fidelity.
video diffusion models LoRA adaptation unified conditioning width and channel wise concatenation geometry-guided self-attention egocentric videos exocentric videos