We address the challenge of recovering an underlying scene geometry and colors from a sparse set of RGBD view observations. In this work, we present a new solution that sequentially generates novel RGBD views along a camera trajectory, and the scene geometry is simply the fusion result of these views. More specifically, we maintain an intermediate surface mesh used for rendering new RGBD views, which subsequently becomes complete by an inpainting network; each rendered RGBD view is later back-projected as a partial surface and is supplemented into the intermediate mesh. The use of intermediate mesh and camera projection helps solve the refractory problem of multi-view inconsistency. We practically implement the RGBD inpainting network as a versatile RGBD diffusion model, which is previously used for 2D generative modeling; we make a modification to its reverse diffusion process to enable our use. We evaluate our approach on the task of 3D scene synthesis from sparse RGBD inputs; extensive experiments on the ScanNet dataset demonstrate the superiority of our approach over existing ones. Project page: https://jblei.site/project-pages/rgbd-diffusion.html

We introduce DiffRF, a novel approach for 3D radiance field synthesis based on denoising diffusion probabilistic models. While existing diffusion-based methods operate on images, latent codes, or point cloud data, we are the first to directly generate volumetric radiance fields. To this end, we propose a 3D denoising model which directly operates on an explicit voxel grid representation. However, as radiance fields generated from a set of posed images can be ambiguous and contain artifacts, obtaining ground truth radiance field samples is non-trivial. We address this challenge by pairing the denoising formulation with a rendering loss, enabling our model to learn a deviated prior that favours good image quality instead of trying to replicate fitting errors like floating artifacts. In contrast to 2D-diffusion models, our model learns multi-view consistent priors, enabling free-view synthesis and accurate shape generation. Compared to 3D GANs, our diffusion-based approach naturally enables conditional generation such as masked completion or single-view 3D synthesis at inference time.

This paper presents a 3D generative model that uses diffusion models to automatically generate 3D digital avatars represented as neural radiance fields. A significant challenge in generating such avatars is that the memory and processing costs in 3D are prohibitive for producing the rich details required for high-quality avatars. To tackle this problem we propose the roll-out diffusion network (Rodin), which represents a neural radiance field as multiple 2D feature maps and rolls out these maps into a single 2D feature plane within which we perform 3D-aware diffusion. The Rodin model brings the much-needed computational efficiency while preserving the integrity of diffusion in 3D by using 3D-aware convolution that attends to projected features in the 2D feature plane according to their original relationship in 3D. We also use latent conditioning to orchestrate the feature generation for global coherence, leading to high-fidelity avatars and enabling their semantic editing based on text prompts. Finally, we use hierarchical synthesis to further enhance details. The 3D avatars generated by our model compare favorably with those produced by existing generative techniques. We can generate highly detailed avatars with realistic hairstyles and facial hair like beards. We also demonstrate 3D avatar generation from image or text as well as text-guided editability.

We propose SparseFusion, a sparse view 3D reconstruction approach that unifies recent advances in neural rendering and probabilistic image generation. Existing approaches typically build on neural rendering with re-projected features but fail to generate unseen regions or handle uncertainty under large viewpoint changes. Alternate methods treat this as a (probabilistic) 2D synthesis task, and while they can generate plausible 2D images, they do not infer a consistent underlying 3D. However, we find that this trade-off between 3D consistency and probabilistic image generation does not need to exist. In fact, we show that geometric consistency and generative inference can be complementary in a mode-seeking behavior. By distilling a 3D consistent scene representation from a view-conditioned latent diffusion model, we are able to recover a plausible 3D representation whose renderings are both accurate and realistic. We evaluate our approach across 51 categories in the CO3D dataset and show that it outperforms existing methods, in both distortion and perception metrics, for sparse-view novel view synthesis.

Virtual reality and augmented reality (XR) bring increasing demand for 3D content. However, creating high-quality 3D content requires tedious work that a human expert must do. In this work, we study the challenging task of lifting a single image to a 3D object and, for the first time, demonstrate the ability to generate a plausible 3D object with 360{\deg} views that correspond well with the given reference image. By conditioning on the reference image, our model can fulfill the everlasting curiosity for synthesizing novel views of objects from images. Our technique sheds light on a promising direction of easing the workflows for 3D artists and XR designers. We propose a novel framework, dubbed NeuralLift-360, that utilizes a depth-aware neural radiance representation (NeRF) and learns to craft the scene guided by denoising diffusion models. By introducing a ranking loss, our NeuralLift-360 can be guided with rough depth estimation in the wild. We also adopt a CLIP-guided sampling strategy for the diffusion prior to provide coherent guidance. Extensive experiments demonstrate that our NeuralLift-360 significantly outperforms existing state-of-the-art baselines. Project page: https://vita-group.github.io/NeuralLift-360/

Generative models, as an important family of statistical modeling, target learning the observed data distribution via generating new instances. Along with the rise of neural networks, deep generative models, such as variational autoencoders (VAEs) and generative adversarial network (GANs), have made tremendous progress in 2D image synthesis. Recently, researchers switch their attentions from the 2D space to the 3D space considering that 3D data better aligns with our physical world and hence enjoys great potential in practice. However, unlike a 2D image, which owns an efficient representation (i.e., pixel grid) by nature, representing 3D data could face far more challenges. Concretely, we would expect an ideal 3D representation to be capable enough to model shapes and appearances in details, and to be highly efficient so as to model high-resolution data with fast speed and low memory cost. However, existing 3D representations, such as point clouds, meshes, and recent neural fields, usually fail to meet the above requirements simultaneously. In this survey, we make a thorough review of the development of 3D generation, including 3D shape generation and 3D-aware image synthesis, from the perspectives of both algorithms and more importantly representations. We hope that our discussion could help the community track the evolution of this field and further spark some innovative ideas to advance this challenging task.

我们研究了从3D对象组成的场景的稀疏源观察的新型视图综合的问题。我们提出了一种简单但有效的方法，既不是持续的也不是隐含的，挑战近期观测综合的趋势。我们的方法将观察显式编码为启用摊销渲染的体积表示。我们证明，虽然由于其表现力，但由于其表现力，但由于其富有力的力量，我们的简单方法获得了与最新的基线的比较比较了与最先进的基线的相当甚至更好的新颖性重建质量，同时增加了渲染速度超过400倍。我们的模型采用类别无关方式培训，不需要特定于场景的优化。因此，它能够将新颖的视图合成概括为在训练期间未见的对象类别。此外，我们表明，通过简单的制定，我们可以使用视图综合作为自我监控信号，以便在没有明确的3D监督的情况下高效学习3D几何。

2D-to-3D reconstruction is an ill-posed problem, yet humans are good at solving this problem due to their prior knowledge of the 3D world developed over years. Driven by this observation, we propose NeRDi, a single-view NeRF synthesis framework with general image priors from 2D diffusion models. Formulating single-view reconstruction as an image-conditioned 3D generation problem, we optimize the NeRF representations by minimizing a diffusion loss on its arbitrary view renderings with a pretrained image diffusion model under the input-view constraint. We leverage off-the-shelf vision-language models and introduce a two-section language guidance as conditioning inputs to the diffusion model. This is essentially helpful for improving multiview content coherence as it narrows down the general image prior conditioned on the semantic and visual features of the single-view input image. Additionally, we introduce a geometric loss based on estimated depth maps to regularize the underlying 3D geometry of the NeRF. Experimental results on the DTU MVS dataset show that our method can synthesize novel views with higher quality even compared to existing methods trained on this dataset. We also demonstrate our generalizability in zero-shot NeRF synthesis for in-the-wild images.

我们引入了一个可扩展的框架，用于从RGB-D图像中具有很大不完整的场景覆盖率的新型视图合成。尽管生成的神经方法在2D图像上表现出了惊人的结果，但它们尚未达到相似的影像学结果，并结合了场景完成，在这种情况下，空间3D场景的理解是必不可少的。为此，我们提出了一条在基于网格的神经场景表示上执行的生成管道，通过以2.5D-3D-2.5D方式进行场景的分布来完成未观察到的场景部分。我们在3D空间中处理编码的图像特征，并具有几何完整网络和随后的纹理镶嵌网络，以推断缺失区域。最终可以通过与一致性的可区分渲染获得感性图像序列。全面的实验表明，我们方法的图形输出优于最新技术，尤其是在未观察到的场景部分中。

Photo-realistic free-viewpoint rendering of real-world scenes using classical computer graphics techniques is challenging, because it requires the difficult step of capturing detailed appearance and geometry models. Recent studies have demonstrated promising results by learning scene representations that implicitly encode both geometry and appearance without 3D supervision. However, existing approaches in practice often show blurry renderings caused by the limited network capacity or the difficulty in finding accurate intersections of camera rays with the scene geometry. Synthesizing high-resolution imagery from these representations often requires time-consuming optical ray marching. In this work, we introduce Neural Sparse Voxel Fields (NSVF), a new neural scene representation for fast and high-quality free-viewpoint rendering. NSVF defines a set of voxel-bounded implicit fields organized in a sparse voxel octree to model local properties in each cell. We progressively learn the underlying voxel structures with a diffentiable ray-marching operation from only a set of posed RGB images. With the sparse voxel octree structure, rendering novel views can be accelerated by skipping the voxels containing no relevant scene content. Our method is typically over 10 times faster than the state-of-the-art (namely, NeRF (Mildenhall et al., 2020)) at inference time while achieving higher quality results. Furthermore, by utilizing an explicit sparse voxel representation, our method can easily be applied to scene editing and scene composition. We also demonstrate several challenging tasks, including multi-scene learning, free-viewpoint rendering of a moving human, and large-scale scene rendering. Code and data are available at our website: https://github.com/facebookresearch/NSVF.

我们提出了Diffustereo，这是一种仅使用稀疏相机（在这项工作中8）进行高质量3D人类重建的新型系统。其核心是一种新型基于扩散的立体声模块，该模块将扩散模型（一种强大的生成模型）引入迭代立体声匹配网络中。为此，我们设计了一个新的扩散内核和其他立体限制，以促进网络中的立体声匹配和深度估计。我们进一步提出了一个多级立体声网络体系结构，以处理高分辨率（最多4K）输入，而无需无法负担的内存足迹。考虑到人类的一组稀疏视图颜色图像，提出的基于多级扩散的立体声网络可以产生高准确的深度图，然后通过有效的多视图融合策略将其转换为高质量的3D人类模型。总体而言，我们的方法可以自动重建人类模型，其质量是高端密集摄像头钻机，这是使用更轻巧的硬件设置来实现的。实验表明，我们的方法在定性和定量上都优于最先进的方法。

In this work, we present a novel framework built to simplify 3D asset generation for amateur users. To enable interactive generation, our method supports a variety of input modalities that can be easily provided by a human, including images, text, partially observed shapes and combinations of these, further allowing to adjust the strength of each input. At the core of our approach is an encoder-decoder, compressing 3D shapes into a compact latent representation, upon which a diffusion model is learned. To enable a variety of multi-modal inputs, we employ task-specific encoders with dropout followed by a cross-attention mechanism. Due to its flexibility, our model naturally supports a variety of tasks, outperforming prior works on shape completion, image-based 3D reconstruction, and text-to-3D. Most interestingly, our model can combine all these tasks into one swiss-army-knife tool, enabling the user to perform shape generation using incomplete shapes, images, and textual descriptions at the same time, providing the relative weights for each input and facilitating interactivity. Despite our approach being shape-only, we further show an efficient method to texture the generated shape using large-scale text-to-image models.

Existing neural rendering methods for creating human avatars typically either require dense input signals such as video or multi-view images, or leverage a learned prior from large-scale specific 3D human datasets such that reconstruction can be performed with sparse-view inputs. Most of these methods fail to achieve realistic reconstruction when only a single image is available. To enable the data-efficient creation of realistic animatable 3D humans, we propose ELICIT, a novel method for learning human-specific neural radiance fields from a single image. Inspired by the fact that humans can easily reconstruct the body geometry and infer the full-body clothing from a single image, we leverage two priors in ELICIT: 3D geometry prior and visual semantic prior. Specifically, ELICIT introduces the 3D body shape geometry prior from a skinned vertex-based template model (i.e., SMPL) and implements the visual clothing semantic prior with the CLIP-based pre-trained models. Both priors are used to jointly guide the optimization for creating plausible content in the invisible areas. In order to further improve visual details, we propose a segmentation-based sampling strategy that locally refines different parts of the avatar. Comprehensive evaluations on multiple popular benchmarks, including ZJU-MoCAP, Human3.6M, and DeepFashion, show that ELICIT has outperformed current state-of-the-art avatar creation methods when only a single image is available. Code will be public for reseach purpose at https://elicit3d.github.io .

Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Before sharing a NeRF, though, it might be desirable to remove personal information or unsightly objects. Such removal is not easily achieved with the current NeRF editing frameworks. We propose a framework to remove objects from a NeRF representation created from an RGB-D sequence. Our NeRF inpainting method leverages recent work in 2D image inpainting and is guided by a user-provided mask. Our algorithm is underpinned by a confidence based view selection procedure. It chooses which of the individual 2D inpainted images to use in the creation of the NeRF, so that the resulting inpainted NeRF is 3D consistent. We show that our method for NeRF editing is effective for synthesizing plausible inpaintings in a multi-view coherent manner. We validate our approach using a new and still-challenging dataset for the task of NeRF inpainting.

我们介绍了Gaudi，Gaudi是一种生成模型，能够捕获可以从移动的相机中沉浸式的复杂和现实3D场景的分布。我们通过一种可扩展而强大的方法解决了这个具有挑战性的问题，我们首先优化了散布辐射场和相机姿势的潜在表示。然后，该潜在表示将学习一个生成模型，该模型可以使3D场景的无条件生成和条件生成。我们的模型概括了以前的作品，该作品通过删除可以在样本中共享相机姿势分布的假设来关注单个对象。我们表明，高迪（Gaudi）在多个数据集的无条件生成设置中获得了最先进的性能，并允许有条件地生成3D场景给定的调理变量，例如稀疏图像观测值或描述场景的文本。

我们重新审视NPBG，这是一种流行的新型视图合成方法，引入了无处不在的点神经渲染范式。我们对具有快速视图合成的数据效率学习特别感兴趣。除前景/背景场景渲染分裂以及改善的损失外，我们还通过基于视图的网状点描述符栅格化来实现这一目标。通过仅在一个场景上训练，我们的表现就超过了在扫描仪上接受过培训的NPBG，然后进行了填充场景。我们还针对最先进的方法SVS进行了竞争性，该方法已在完整的数据集（DTU，坦克和寺庙）上进行了培训，然后进行了对现场的培训，尽管它们具有更深的神经渲染器。

我们提出了一种学习方法，可以从单个视图开始生成自然场景的无界飞行视频，在该视图中，从单个照片集中学习了这种功能，而无需每个场景的相机姿势甚至多个视图。为了实现这一目标，我们提出了一种新颖的自我监督视图生成训练范式，在这里我们采样和渲染虚拟摄像头轨迹，包括循环轨迹，使我们的模型可以从单个视图集合中学习稳定的视图生成。在测试时，尽管在训练过程中从未见过视频，但我们的方法可以拍摄单个图像，并产生长的相机轨迹，包括数百个新视图，具有现实和多样化的内容。我们将我们的方法与最新的监督视图生成方法进行了比较，该方法需要摆姿势的多视频视频，并展示了卓越的性能和综合质量。

Conditional diffusion probabilistic models can model the distribution of natural images and can generate diverse and realistic samples based on given conditions. However, oftentimes their results can be unrealistic with observable color shifts and textures. We believe that this issue results from the divergence between the probabilistic distribution learned by the model and the distribution of natural images. The delicate conditions gradually enlarge the divergence during each sampling timestep. To address this issue, we introduce a new method that brings the predicted samples to the training data manifold using a pretrained unconditional diffusion model. The unconditional model acts as a regularizer and reduces the divergence introduced by the conditional model at each sampling step. We perform comprehensive experiments to demonstrate the effectiveness of our approach on super-resolution, colorization, turbulence removal, and image-deraining tasks. The improvements obtained by our method suggest that the priors can be incorporated as a general plugin for improving conditional diffusion models.

Text-guided diffusion models have shown superior performance in image/video generation and editing. While few explorations have been performed in 3D scenarios. In this paper, we discuss three fundamental and interesting problems on this topic. First, we equip text-guided diffusion models to achieve $\textbf{3D-consistent generation}$. Specifically, we integrate a NeRF-like neural field to generate low-resolution coarse results for a given camera view. Such results can provide 3D priors as condition information for the following diffusion process. During denoising diffusion, we further enhance the 3D consistency by modeling cross-view correspondences with a novel two-stream (corresponding to two different views) asynchronous diffusion process. Second, we study $\textbf{3D local editing}$ and propose a two-step solution that can generate 360$^{\circ}$ manipulated results by editing an object from a single view. Step 1, we propose to perform 2D local editing by blending the predicted noises. Step 2, we conduct a noise-to-text inversion process that maps 2D blended noises into the view-independent text embedding space. Once the corresponding text embedding is obtained, 360$^{\circ}$ images can be generated. Last but not least, we extend our model to perform \textbf{one-shot novel view synthesis} by fine-tuning on a single image, firstly showing the potential of leveraging text guidance for novel view synthesis. Extensive experiments and various applications show the prowess of our 3DDesigner. The project page is available at https://3ddesigner-diffusion.github.io/.

随着信息中的各种方式存在于现实世界中的各种方式，多式联信息之间的有效互动和融合在计算机视觉和深度学习研究中的多模式数据的创造和感知中起着关键作用。通过卓越的功率，在多式联运信息中建模互动，多式联运图像合成和编辑近年来已成为一个热门研究主题。与传统的视觉指导不同，提供明确的线索，多式联路指南在图像合成和编辑方面提供直观和灵活的手段。另一方面，该领域也面临着具有固有的模态差距的特征的几个挑战，高分辨率图像的合成，忠实的评估度量等。在本调查中，我们全面地阐述了最近多式联运图像综合的进展根据数据模型和模型架构编辑和制定分类。我们从图像合成和编辑中的不同类型的引导方式开始介绍。然后，我们描述了多模式图像综合和编辑方法，其具有详细的框架，包括生成的对抗网络（GAN），GaN反转，变压器和其他方法，例如NERF和扩散模型。其次是在多模式图像合成和编辑中广泛采用的基准数据集和相应的评估度量的综合描述，以及分析各个优点和限制的不同合成方法的详细比较。最后，我们为目前的研究挑战和未来的研究方向提供了深入了解。与本调查相关的项目可在HTTPS://github.com/fnzhan/mise上获得