SceneGen: Single-Image 3D Scene Generation in One Feedforward Pass
3D content generation has recently attracted significant research interest
due to its applications in VR/AR and embodied AI. In this work, we address the
challenging task of synthesizing multiple 3D assets within a single scene
image. Concretely, our contributions are fourfold: (i) we present SceneGen, a
novel framework that takes a scene image and corresponding object masks as
input, simultaneously producing multiple 3D assets with geometry and texture.
Notably, SceneGen operates with no need for optimization or asset retrieval;
(ii) we introduce a novel feature aggregation module that integrates local and
global scene information from visual and geometric encoders within the feature
extraction module. Coupled with a position head, this enables the generation of
3D assets and their relative spatial positions in a single feedforward pass;
(iii) we demonstrate SceneGen's direct extensibility to multi-image input
scenarios. Despite being trained solely on single-image inputs, our
architectural design enables improved generation performance with multi-image
inputs; and (iv) extensive quantitative and qualitative evaluations confirm the
efficiency and robust generation abilities of our approach. We believe this
paradigm offers a novel solution for high-quality 3D content generation,
potentially advancing its practical applications in downstream tasks. The code
and model will be publicly available at: https://mengmouxu.github.io/SceneGen.
68a7cd5939413c456c05b09e
Yanxu Meng
claimed_verified
2025-08-26T19:06:35.644Z
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Mengmouxu
user
Mengmouxu
68a7cd5939413c456c05b09f
Haoning Wu
claimed_verified
2025-08-22T07:22:01.228Z
632c7a0d1d303f5f9acf01b8
https://cdn-avatars.huggingface.co/v1/production/uploads/632c7a0d1d303f5f9acf01b8/T010IFuCp6UaOeIyWhbCk.jpeg
Haoning Wu
user
haoningwu
68a7cd5939413c456c05b0a0
Ya Zhang
68a7cd5939413c456c05b0a1
Weidi Xie
admin_assigned
2025-08-22T08:27:57.412Z
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Xie
user
Weidi
SceneGen: Single-Image 3D Scene Generation in One Feedforward Pass
3D content generation has recently attracted significant research interest
due to its applications in VR/AR and embodied AI. In this work, we address the
challenging task of synthesizing multiple 3D assets within a single scene
image. Concretely, our contributions are fourfold: (i) we present SceneGen, a
novel framework that takes a scene image and corresponding object masks as
input, simultaneously producing multiple 3D assets with geometry and texture.
Notably, SceneGen operates with no need for optimization or asset retrieval;
(ii) we introduce a novel feature aggregation module that integrates local and
global scene information from visual and geometric encoders within the feature
extraction module. Coupled with a position head, this enables the generation of
3D assets and their relative spatial positions in a single feedforward pass;
(iii) we demonstrate SceneGen's direct extensibility to multi-image input
scenarios. Despite being trained solely on single-image inputs, our
architectural design enables improved generation performance with multi-image
inputs; and (iv) extensive quantitative and qualitative evaluations confirm the
efficiency and robust generation abilities of our approach. We believe this
paradigm offers a novel solution for high-quality 3D content generation,
potentially advancing its practical applications in downstream tasks. The code
and model will be publicly available at: https://mengmouxu.github.io/SceneGen.
SceneGen generates multiple 3D assets from a single scene image using a novel framework that integrates local and global scene information, enabling efficient and robust 3D content creation.
SceneGen feature aggregation module visual encoders geometric encoders position head multi-image input 3D content generation VR/AR embodied AI