Learning 3D models of all animals on the Earth requires massively scaling up existing solutions. With this ultimate goal in mind, we develop 3D-Fauna, an approach that learns a pan-category deformable 3D animal model for more than 100 animal species jointly. One crucial bottleneck of modeling animals is the limited availability of training data, which we overcome by simply learning from 2D Internet images. We show that prior category-specific attempts fail to generalize to rare species with limited training images. We address this challenge by introducing the Semantic Bank of Skinned Models (SBSM), which automatically discovers a small set of base animal shapes by combining geometric inductive priors with semantic knowledge implicitly captured by an off-the-shelf self-supervised feature extractor. To train such a model, we also contribute a new large-scale dataset of diverse animal species. At inference time, given a single image of any quadruped animal, our model reconstructs an articulated 3D mesh in a feed-forward fashion within seconds.
Recently, 3D generative models have made impressive progress, enabling the generation of almost arbitrary 3D assets from text or image inputs. However, these approaches generate objects in isolation without any consideration for the scene where they will eventually be placed. In this paper, we propose a framework that allows for the stylization of an existing 3D asset to fit into a given 2D scene, and additionally produce a photorealistic composition as if the asset was placed within the environment. This not only opens up a new level of control for object stylization, for example, the same assets can be stylized to reflect changes in the environment, such as summer to winter or fantasy versus futuristic settings-but also makes the object-scene composition more controllable. We achieve this by combining modeling and optimizing the object's texture and environmental lighting through differentiable ray tracing with image priors from pre-trained text-to-image diffusion models. We demonstrate that our method is applicable to a wide variety of indoor and outdoor scenes and arbitrary objects.
Neural Radiance Fields have achieved remarkable results for novel view synthesis but still lack a crucial component: precise measurement of uncertainty in their predictions. Probabilistic NeRF methods have tried to address this, but their output probabilities are not typically accurately calibrated, and therefore do not capture the true confidence levels of the model. Calibration is a particularly challenging problem in the sparse-view setting, where additional held-out data is unavailable for fitting a calibrator that generalizes to the test distribution. In this paper, we introduce the first method for obtaining calibrated uncertainties from NeRF models. Our method is based on a robust and efficient metric to calculate per-pixel uncertainties from the predictive posterior distribution. We propose two techniques that eliminate the need for held-out data. The first, based on patch sampling, involves training two NeRF models for each scene. The second is a novel meta-calibrator that only requires the training of one NeRF model. Our proposed approach for obtaining calibrated uncertainties achieves state-of-the-art uncertainty in the sparse-view setting while maintaining image quality. We further demonstrate our method's effectiveness in applications such as view enhancement and next-best view selection.
We present Farm3D, a method to learn category-specific 3D reconstructors for articulated objects entirely from "free" virtual supervision from a pre-trained 2D diffusion-based image generator. Recent approaches can learn, given a collection of single-view images of an object category, a monocular network to predict the 3D shape, albedo, illumination and viewpoint of any object occurrence. We propose a framework using an image generator like Stable Diffusion to generate virtual training data for learning such a reconstruction network from scratch. Furthermore, we include the diffusion model as a score to further improve learning. The idea is to randomise some aspects of the reconstruction, such as viewpoint and illumination, generating synthetic views of the reconstructed 3D object, and have the 2D network assess the quality of the resulting image, providing feedback to the reconstructor. Different from work based on distillation which produces a single 3D asset for each textual prompt in hours, our approach produces a monocular reconstruction network that can output a controllable 3D asset from a given image, real or generated, in only seconds. Our network can be used for analysis, including monocular reconstruction, or for synthesis, generating articulated assets for real-time applications such as video games.
We consider the problem of learning a function that can estimate the 3D shape, articulation, viewpoint, texture, and lighting of an articulated animal like a horse, given a single test image. We present a new method, dubbed MagicPony, that learns this function purely from in-the-wild single-view images of the object category, with minimal assumptions about the topology of deformation. At its core is an implicit-explicit representation of articulated shape and appearance, combining the strengths of neural fields and meshes. In order to help the model understand an object's shape and pose, we distil the knowledge captured by an off-the-shelf self-supervised vision transformer and fuse it into the 3D model. To overcome common local optima in viewpoint estimation, we further introduce a new viewpoint sampling scheme that comes at no added training cost. Compared to prior works, we show significant quantitative and qualitative improvements on this challenging task. The model also demonstrates excellent generalisation in reconstructing abstract drawings and artefacts, despite the fact that it is only trained on real images.
Learning deformable 3D objects from 2D images is an extremely ill-posed problem. Existing methods rely on explicit supervision to establish multi-view correspondences, such as template shape models and keypoint annotations, which restricts their applicability on objects "in the wild". In this paper, we propose to use monocular videos, which naturally provide correspondences across time, allowing us to learn 3D shapes of deformable object categories without explicit keypoints or template shapes. Specifically, we present DOVE, which learns to predict 3D canonical shape, deformation, viewpoint and texture from a single 2D image of a bird, given a bird video collection as well as automatically obtained silhouettes and optical flows as training data. Our method reconstructs temporally consistent 3D shape and deformation, which allows us to animate and re-render the bird from arbitrary viewpoints from a single image.
We introduce KeypointDeformer, a novel unsupervised method for shape control through automatically discovered 3D keypoints. We cast this as the problem of aligning a source 3D object to a target 3D object from the same object category. Our method analyzes the difference between the shapes of the two objects by comparing their latent representations. This latent representation is in the form of 3D keypoints that are learned in an unsupervised way. The difference between the 3D keypoints of the source and the target objects then informs the shape deformation algorithm that deforms the source object into the target object. The whole model is learned end-to-end and simultaneously discovers 3D keypoints while learning to use them for deforming object shapes. Our approach produces intuitive and semantically consistent control of shape deformations. Moreover, our discovered 3D keypoints are consistent across object category instances despite large shape variations. As our method is unsupervised, it can be readily deployed to new object categories without requiring annotations for 3D keypoints and deformations.
We introduce a method for learning landmark detectors from unlabelled video frames and unpaired labels. This allows us to learn a detector from a large collection of raw videos given only a few example annotations harvested from existing data or motion capture. We achieve this by formulating the landmark detection task as one of image translation, learning to map an image of the object to an image of its landmarks, represented as a skeleton. The advantage is that this translation problem can then be tackled by CycleGAN. However, we show that a naive application of CycleGAN confounds appearance and pose information, with suboptimal keypoint detection performance. We solve this problem by introducing an analytical and differentiable renderer for the skeleton image so that no appearance information can be leaked in the skeleton. Then, since cycle consistency requires to reconstruct the input image from the skeleton, we supply the appearance information thus removed by conditioning the generator with a second image of the same object (e.g. another frame from a video). Furthermore, while CycleGAN uses two cycle consistency constraints, we show that the second one is detrimental in this application and we discard it, significantly simplifying the model. We show that these modifications improve the quality of the learned detector leading to state-of-the-art unsupervised landmark detection performance in a number of challenging human pose and facial landmark detection benchmarks.
In this paper, we consider the problem of learning landmarks for object categories without any manual annotations. We cast this as the problem of conditionally generating an image of an object from another one, where the images differ by acquisition time and/or viewpoint. The process is aided by providing the generator with a keypoint-like representation extracted from the target image through a tight bottleneck. This encourages the representation to distil information about the object geometry, which changes from source to target, while the appearance, which is shared between the source and target, is read off from the source alone. Conditioning simplifies the generation task significantly, to the point that adopting a simple perceptual loss instead of more sophisticated approaches such as adversarial training is sufficient to learn landmarks. We show that our method is applicable to a large variety of datasets - faces, people, 3D objects, and digits - without any modifications. We further demonstrate that we can learn landmarks from synthetic image deformations or videos, all without manual supervision, while outperforming state-of-the-art unsupervised landmark detectors.