SK-Adapter: Skeleton-Based Structural Control for Native 3D Generation
Native 3D generative models have achieved remarkable fidelity and speed, yet they suffer from a critical limitation: inability to prescribe precise structural articulations, where precise structural control within the native 3D space remains underexplored. This paper proposes SK-Adapter, a simple and yet highly efficient and effective framework that unlocks precise skeletal manipulation for native 3D generation. Moving beyond text or image prompts, which can be ambiguous for precise structure, we treat the 3D skeleton as a first-class control signal. SK-Adapter is a lightweight structural adapter network that encodes joint coordinates and topology into learnable tokens, which are injected into the frozen 3D generation backbone via cross-attention. This smart design allows the model to not only effectively "attend" to specific 3D structural constraints but also preserve its original generative priors. To bridge the data gap, we contribute Objaverse-TMS dataset, a large-scale dataset of 24k text-mesh-skeleton pairs. Extensive experiments confirm that our method achieves robust structural control while preserving the geometry and texture quality of the foundation model, significantly outperforming existing baselines. Furthermore, we extend this capability to local 3D editing, enabling the region specific editing of existing assets with skeletal guidance, which is unattainable by previous methods. Project Page: https://sk-adapter.github.io/
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Anbang Wang
claimed_verified
2026-03-17T17:33:50.037Z
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WANG, Anbang
aawangas
user
aawangas
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Yuzhuo Ao
claimed_verified
2026-03-17T17:33:52.355Z
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Yuzhuo Ao
Supramundaner
user
Supramundaner
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Shangzhe Wu
admin_assigned
2026-03-18T10:07:52.104Z
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Shangzhe Wu
Shangzhe01
user
Shangzhe01
69b96061ebc57f8925831f0d
Chi-Keung Tang
SK-Adapter: Skeleton-Based Structural Control for Native 3D Generation
Native 3D generative models have achieved remarkable fidelity and speed, yet they suffer from a critical limitation: inability to prescribe precise structural articulations, where precise structural control within the native 3D space remains underexplored. This paper proposes SK-Adapter, a simple and yet highly efficient and effective framework that unlocks precise skeletal manipulation for native 3D generation. Moving beyond text or image prompts, which can be ambiguous for precise structure, we treat the 3D skeleton as a first-class control signal. SK-Adapter is a lightweight structural adapter network that encodes joint coordinates and topology into learnable tokens, which are injected into the frozen 3D generation backbone via cross-attention. This smart design allows the model to not only effectively "attend" to specific 3D structural constraints but also preserve its original generative priors. To bridge the data gap, we contribute Objaverse-TMS dataset, a large-scale dataset of 24k text-mesh-skeleton pairs. Extensive experiments confirm that our method achieves robust structural control while preserving the geometry and texture quality of the foundation model, significantly outperforming existing baselines. Furthermore, we extend this capability to local 3D editing, enabling the region specific editing of existing assets with skeletal guidance, which is unattainable by previous methods. Project Page: https://sk-adapter.github.io/
SK-Adapter enables precise 3D structural control by treating skeletons as direct inputs through a lightweight adapter network that injects learnable tokens into frozen 3D generation models via cross-attention mechanisms.
3D generation skeletal manipulation structural control cross-attention learnable tokens 3D generation backbone Objaverse-TMS dataset local 3D editing