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Excited to share our work on Neural Assets: a new method for enabling 3D asset-level control in image diffusion models – scalable & without any 3D inductive biases. Neural Assets goes beyond text or pixel-based control & provides an interface inspired by 3D graphics tools. 🧵

Thomas Kipf

29,089 subscribers

97,924 görüntüleme • 2 yıl önce •via X (Twitter)

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Thomas Kipf profil fotoğrafı

Thomas Kipf2 yıl önce

Paper: Website: Neural Assets enables a range of 3D editing capabilities for individual or multiple assets: translation, rotation, rescaling, transfer across scenes, and control over the scene background.

Thomas Kipf profil fotoğrafı

Thomas Kipf2 yıl önce

Assets extracted from one scene and placed into a different scene or background naturally adapt to lighting conditions and other environmental factors. At night or in rainy conditions, cars even turn their lights/headlights on!

Thomas Kipf profil fotoğrafı

Thomas Kipf2 yıl önce

Neural Assets are extracted from raw video frames with the help of 2D or 3D boxes. The key to make it work is to extract appearance and pose representations from *different* frames, which results in disentanglement and thus controllability. The entire model is trained/fine-tuned end-to-end jointly with a pre-trained image generation model (here: Stable Diffusion 2.1) simply by replacing the text token sequence in the base text-to-image model with a Neural Assets token sequence. At test time, we can compose scenes by combining multiple Neural Assets and a neural representation of the scene background, while ensuring appearance consistency (to a large extent) both for individual assets as well as the overall scene.

Thomas Kipf profil fotoğrafı

Thomas Kipf2 yıl önce

Check out our paper ( and our website ( for a lot more details, results, and current limitations / failure modes. Neural Assets is the result of @Dazitu_616's outstanding work as a student researcher in our team, working with a set of fantastic collaborators: @YuliaRubanova, @RishabhKabra, @drewAhudson, @yusufaytar, @vansteenkiste_s, @KelseyRAllen; advised by @igilitschenski. Starting with Slot Attention in 2020, we have pursued this research direction over the past four years (SAVi, OSRT, DORSal & many other works). I couldn't be more excited about this latest result and the potential for this class of methods to enable new creative control capabilities for image generation models and beyond.

Yulia Rubanova profil fotoğrafı

Yulia Rubanova2 yıl önce

Super excited to be part of this work. Using the same interface of Neural Assets, we can get a rich set of controls over the objects and seamlessly blend the objects into the environment, with appropriate lighting and shadows. Amazing work, @Dazitu_616!

Ziyi Wu profil fotoğrafı

Ziyi Wu2 yıl önce

Thank you, Thomas! It's been an awesome experience working with you and all the Google folks. I will definitely miss this Student Researcher journey!

Omri Kaduri profil fotoğrafı

Omri Kaduri2 yıl önce

That's really great to see the progress you are making on object-centric representations. Does this model and code will be released?

Nate Codes profil fotoğrafı

Nate Codes1 yıl önce

When people can do this inside of the physical neural asset space things I'm going to be lost in AR/VR! I love your work and it has tons of implications for my work.

Thomas Kipf profil fotoğrafı

Thomas Kipf1 yıl önce

Thanks, Nate! Lots of work still to be done by the machine learning community before an approach like this becomes widely usable, but I’m personally really excited about this future.

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Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite Imagery TL;DR: Skyfall-GS converts satellite images to explorable 3D urban scenes using diffusion models, with real-time rendering performance. Contributions: • We introduce Skyfall-GS, the first method to synthesize immersive, real-time, free-flight navigable 3D urban scenes solely from multi-view satellite imagery using generative refinement. • An open-domain refinement approach leverages pre-trained text-to-image diffusion models without domain-specific training. • A curriculum-learning-based iterative refinement strategy progressively enhances reconstruction quality from higher to lower viewpoints, significantly improving visual fidelity in occluded areas.

MrNeRF

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MVDream: Multi-view Diffusion for 3D Generation paper page: propose MVDream, a multi-view diffusion model that is able to generate geometrically consistent multi-view images from a given text prompt. By leveraging image diffusion models pre-trained on large-scale web datasets and a multi-view dataset rendered from 3D assets, the resulting multi-view diffusion model can achieve both the generalizability of 2D diffusion and the consistency of 3D data. Such a model can thus be applied as a multi-view prior for 3D generation via Score Distillation Sampling, where it greatly improves the stability of existing 2D-lifting methods by solving the 3D consistency problem. Finally, we show that the multi-view diffusion model can also be fine-tuned under a few shot setting for personalized 3D generation, i.e. DreamBooth3D application, where the consistency can be maintained after learning the subject identity.

AK

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Lossfunk

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Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.

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Painting with 3D Gaussian Splat Brushes Contributions: • Designing a set of interactive tools and brush parameters for artistic brush creation and control using 3DGS content. • Computing oriented 3D Gaussian splat brushes for stamp-based painting on 3D surfaces represented as meshes or 3DGS scenes. • Deforming the splats in a brush stamp to ensure a smooth appearance of the painted 3D stroke. • Producing seamless brush strokes despite overlapping brush stamps, using diffusion inpainting. • Efficient modeling and rendering of brush strokes to facilitate 3DGS painting in real time.

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