Sketch2NeRF: Multi-view Sketch-guided Text-to-3D Generation
Recently, text-to-3D approaches have achieved high-fidelity 3D content
generation using text description. However, the generated objects are
stochastic and lack fine-grained control. Sketches provide a cheap approach to
introduce such fine-grained control. Nevertheless, it is challenging to achieve
flexible control from these sketches due to their abstraction and ambiguity. In
this paper, we present a multi-view sketch-guided text-to-3D generation
framework (namely, Sketch2NeRF) to add sketch control to 3D generation.
Specifically, our method leverages pretrained 2D diffusion models (e.g., Stable
Diffusion and ControlNet) to supervise the optimization of a 3D scene
represented by a neural radiance field (NeRF). We propose a novel synchronized
generation and reconstruction method to effectively optimize the NeRF. In the
experiments, we collected two kinds of multi-view sketch datasets to evaluate
the proposed method. We demonstrate that our method can synthesize 3D
consistent contents with fine-grained sketch control while being high-fidelity
to text prompts. Extensive results show that our method achieves
state-of-the-art performance in terms of sketch similarity and text alignment.
65b31715c8a577067d293198
Minglin Chen
admin_assigned
2024-01-26T10:02:56.795Z
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/avatars/54a7b2a20ea9661b188a23a9e4fbdfd4.svg
Minglin Chen
user
mlchen
65b31715c8a577067d293199
Longguang Wang
65b31715c8a577067d29319a
Weihao Yuan
admin_assigned
2024-01-26T10:03:33.317Z
649e7793f8565ca45f6a18d1
/avatars/50b75a435a3baf5ec0de7259f19fb9f8.svg
Weihao Yuan
user
willsky
65b31715c8a577067d29319b
Yukun Wang
65b31715c8a577067d29319c
Zhe Sheng
65b31715c8a577067d29319d
Yisheng He
65b31715c8a577067d29319e
Zilong Dong
admin_assigned
2024-01-26T10:04:25.750Z
6582878f97c071ac05da130b
/avatars/b4265ec4eaafff5b0f7b44f983a1cb0e.svg
Zilong Dong
user
zjudzl
65b31715c8a577067d29319f
Liefeng Bo
admin_assigned
2024-01-26T10:04:37.381Z
63d0cc736b985b0f25d0412c
/avatars/3eb8c79f9a7c4c819038ea7b04e323dd.svg
Bo
user
Liefeng
65b31715c8a577067d2931a0
Yulan Guo
Sketch2NeRF: Multi-view Sketch-guided Text-to-3D Generation
Recently, text-to-3D approaches have achieved high-fidelity 3D content
generation using text description. However, the generated objects are
stochastic and lack fine-grained control. Sketches provide a cheap approach to
introduce such fine-grained control. Nevertheless, it is challenging to achieve
flexible control from these sketches due to their abstraction and ambiguity. In
this paper, we present a multi-view sketch-guided text-to-3D generation
framework (namely, Sketch2NeRF) to add sketch control to 3D generation.
Specifically, our method leverages pretrained 2D diffusion models (e.g., Stable
Diffusion and ControlNet) to supervise the optimization of a 3D scene
represented by a neural radiance field (NeRF). We propose a novel synchronized
generation and reconstruction method to effectively optimize the NeRF. In the
experiments, we collected two kinds of multi-view sketch datasets to evaluate
the proposed method. We demonstrate that our method can synthesize 3D
consistent contents with fine-grained sketch control while being high-fidelity
to text prompts. Extensive results show that our method achieves
state-of-the-art performance in terms of sketch similarity and text alignment.
A multi-view sketch-guided text-to-3D generation framework (Sketch2NeRF) uses pretrained 2D diffusion models and NeRF to produce high-fidelity 3D content with fine-grained sketch control, achieving state-of-the-art performance in sketch similarity and text alignment.
Stable Diffusion ControlNet neural radiance field NeRF synchronized generation reconstruction multi-view sketch datasets