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Richard Tucker

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    Simple and Effective Synthesis of Indoor 3D Scenes
    Jing Yu Koh
    Harsh Agrawal
    Dhruv Batra
    Honglak Lee
    Yinfei Yang
    Peter Anderson
    AAAI (2023) (to appear)
    Preview abstract We study the problem of synthesizing immersive 3D indoor scenes from one or a few images. Our aim is to generate high-resolution images and videos from novel viewpoints, including viewpoints that extrapolate far beyond the input images while maintaining 3D consistency. Existing approaches are highly complex, with many separately trained stages and components. We propose a simple alternative: an image-to-image GAN that maps directly from reprojections of incomplete point clouds to full high-resolution RGB-D images. On the Matterport3D and RealEstate10K datasets, our approach significantly outperforms prior work when evaluated by humans, as well as on FID scores. Further, we show that our model is useful for generative data augmentation. A visionand-language navigation (VLN) agent trained with trajectories spatially-perturbed by our model improves success rate by up to 1.5% over a state of the art baseline on the mature R2R benchmark. Our code is publicly released to facilitate generative data augmentation and applications to downstream robotics and embodied AI tasks. View details
    DynIBaR: Neural Dynamic Image-Based Rendering
    Zhengqi Li
    Qianqian Wang
    Computer Vision and Pattern Recognition (CVPR) (2023)
    Preview abstract We address the problem of synthesizing novel views from a monocular video depicting complex dynamic scenes. State-of-the-art methods based on temporally varying Neural Radiance Fields (aka \emph{dynamic NeRFs}) have shown impressive results on this task. However, for long videos with complex object motions and uncontrolled camera trajectories, these methods can produce blurry or inaccurate renderings, hampering their use in real-world applications. Rather than encoding a dynamic scene within the weights of MLPs, we present a new method that addresses these limitations by adopting a volumetric image-based rendering framework that synthesizes new viewpoints by aggregating features from nearby views in a scene-motion-aware manner. Our system preserves the advantages for modeling complex scenes and view-dependent effects, but enables synthesizing photo-realistic novel views from long videos featuring complex scene dynamics with unconstrained camera trajectories. We demonstrate significant improvements over state-of-the-art methods on dynamic scene datasets, and also apply our approach to in-the-wild videos with challenging camera and object motion, where prior methods fail to produce high-quality renderings. View details
    Persistent Nature: A Generative Model of Unbounded 3D Worlds
    Lucy Chai
    Zhengqi Li
    Phillip Isola
    Computer Vision and Pattern Recognition (CVPR) (2023)
    Preview abstract Despite increasingly realistic image quality, recent 3D image generative models often operate on bounded domains with limited camera motions. We investigate the task of unconditionally synthesizing unbounded nature scenes, enabling arbitrarily large camera motion while maintaining a persistent 3D world model. Our scene representation consists of an extendable, planar scene layout grid, which can be rendered from arbitrary camera poses via a 3D decoder and volume rendering, and a panoramic skydome. Based on this representation, we learn a generative world model solely from single-view internet photos. Our method enables simulating long flights through 3D landscapes, while maintaining global scene consistency--for instance, returning to the starting point yields the same view of the scene. Our approach enables scene extrapolation beyond the fixed bounds of current 3D generative models, while also supporting a persistent, camera-independent world representation that stands in contrast to auto-regressive 3D prediction models. View details
    Deformable Sprites for Unsupervised Video Decomposition
    Vickie Ye
    Zhengqi Li
    Angjoo Kanazawa
    Computer Vision and Pattern Recognition (CVPR) (2022)
    Preview abstract We describe a method to extract persistent elements of a dynamic scene from an input video. We represent each scene element as a Deformable Sprite consisting of three components: 1) a 2D texture image for the entire video, 2) per-frame masks for the element, and 3) non-rigid deformations that map the texture image into each video frame. The resulting decomposition allows for applications such as consistent video editing. Deformable Sprites are a type of video auto-encoder model that is optimized on individual videos, and does not require training on a large dataset, nor does it rely on pre-trained models. Moreover, our method does not require object masks or other user input, and discovers moving objects of a wider variety than previous work. We evaluate our approach on standard video datasets and show qualitative results on a diverse array of Internet videos. View details
    Dimensions of Motion: Monocular Prediction through Flow Subspaces
    Richard Strong Bowen*
    Ramin Zabih
    Proceedings of the International Conference on 3D Vision (3DV) (2022)
    Preview abstract We introduce a way to learn to estimate a scene representation from a single image by predicting a low-dimensional subspace of optical flow for each training example, which encompasses the variety of possible camera and object movement. Supervision is provided by a novel loss which measures the distance between this predicted flow subspace and an observed optical flow. This provides a new approach to learning scene representation tasks, such as monocular depth prediction or instance segmentation, in an unsupervised fashion using in-the-wild input videos without requiring camera poses, intrinsics, or an explicit multi-view stereo step. We evaluate our method in multiple settings, including an indoor depth prediction task where it achieves comparable performance to recent methods trained with more supervision. View details
    Preview abstract Single image 3D photography enables viewers to view a still image from novel viewpoints. Recent approaches for single-image view synthesis combine monocular depth network along with inpainting networks resulting in compelling novel view synthesis results. A drawback of these approaches is the use of hard layering making them not suitable to model intricate appearance effects such as matting. We present SLIDE, a modular and unified system for single image 3D photography that uses simple yet effective soft layering strategy to model appearance effects. In addition, we propose a novel depth-aware training of inpainting network suitable for 3D photography task. Extensive experimental analysis on 3 different view synthesis datasets in combination with user studies on in-the-wild image collections demonstrate the superior performance of our technique in comparison to existing strong baselines. View details
    KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control
    Tomas Jakab
    Jiajun Wu
    Angjoo Kanazawa
    Computer Vision and Pattern Recognition (CVPR) (2021)
    Preview abstract We present KeypointDeformer, a novel unsupervised method for shape control through automatically discovered 3D keypoints. 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. Since our method is unsupervised, it can be readily deployed to new object categories without requiring expensive annotations for 3D keypoints and deformations. View details
    Preview abstract We introduce the problem of perpetual view generation—long-range generation of novel views corresponding to an arbitrarily long camera trajectory given a single image. This is a challenging problem that goes far beyond the capabilities of current view synthesis methods, which work for a limited range of viewpoints and quickly degenerate when presented with a large camera motion. Methods designed for video generation also have limited ability to produce long video sequences and are often agnostic to scene geometry. We take a hybrid approach that integrates both geometry and image synthesis in an iterative render, refine, and repeat framework, allowing for long-range generation that cover large distances after hundreds of frames. Our approach can be trained from a set of monocular video sequences without any manual annotation. We propose a dataset of aerial footage of natural coastal scenes, and compare our method with recent view synthesis and conditional video generation baselines, showing that it can generate plausible scenes for much longer time horizons over large camera trajectories compared to existing methods. View details
    De-rendering the World’s Revolutionary Artefacts
    Elliott Wu
    Jiajun Wu
    Angjoo Kanazawa
    Computer Vision and Pattern Recognition (CVPR) (2021)
    Preview abstract Recent works have shown exciting results in unsupervised image de-rendering—learning to decompose 3D shape, appearance, and lighting from single-image collections without explicit supervision. However, many of these assume simplistic material and lighting models. We propose a method, termed RADAR (Revolutionary Artefact De-rendering And Re-rendering), that can recover environment illumination and surface materials from real single-image collections, relying neither on explicit 3D supervision, nor on multi-view or multi-light images. Specifically, we focus on rotationally symmetric artefacts that exhibit challenging surface properties including specular reflections, such as vases. We introduce a novel self-supervised albedo discriminator, which allows the model to recover plausible albedo without requiring any ground-truth during training. In conjunction with a shape reconstruction module exploiting rotational symmetry, we present an end-to-end learning framework that is able to de-render the world's revolutionary artefacts. We conduct experiments on a real vase dataset and demonstrate compelling decomposition results, allowing for applications including free-viewpoint rendering and relighting. View details
    Preview abstract A recent strand of work in view synthesis uses deep learning to generate multiplane images—a camera-centric, layered 3D representation—given two or more input images at known viewpoints. We apply this representation to single-view view synthesis, a problem which is more challenging but has potentially much wider application. Our method learns to produce a multiplane image directly from a single image input, predicting shape and disoccluded content in a single step, and we introduce scale-invariant view synthesis for supervision, enabling us to train on online video. We show this approach is applicable to several different datasets, that it additionally generates reasonable depth maps, and that it learns to fill in content behind the edges of foreground objects in background layers. View details
    MetaSDF: Meta-Learning Signed Distance Functions
    Vincent Sitzmann
    Eric R. Chan
    Gordon Wetzstein
    NeurIPS 2020
    Preview abstract Neural implicit shape representations are an emerging paradigm that offers many potential benefits over conventional discrete representations, including memory efficiency at a high spatial resolution. Generalizing across shapes with such neural implicit representations amounts to learning priors over the respective function space and enables geometry reconstruction from partial or noisy observations. Existing generalization methods rely on conditioning a neural network on a low-dimensional latent code that is either regressed by an encoder or jointly optimized in the auto-decoder framework. Here, we formalize learning of a shape space as a meta-learning problem and leverage gradient-based meta-learning algorithms to solve this task. We demonstrate that this approach performs on par with auto-decoder based approaches while being an order of magnitude faster at test-time inference. We further demonstrate that the proposed gradient-based method outperforms encoder-decoder based methods that leverage pooling-based set encoders. View details
    Preview abstract We present a deep learning solution for estimating the incident illumination at any 3D location within a scene from an input narrow-baseline stereo image pair. Previous approaches for predicting global illumination from images either predict just a single illumination for the entire scene, or separately estimate the illumination at each 3D location without enforcing that the predictions are consistent with the same 3D scene. Instead, we propose a deep learning model that estimates a 3D volumetric RGBA model of a scene, including content outside the observed field of view, and then uses standard volume rendering to estimate the incident illumination at any 3D location within that volume. Our model is trained without any ground truth 3D data and only requires a held-out perspective view near the input stereo pair and a spherical panorama taken within each scene as supervision, as opposed to prior methods for spatially-varying lighting estimation, which require ground truth scene geometry for training. We demonstrate that our method can predict consistent spatially-varying lighting that is convincing enough to plausibly relight and insert highly specular virtual objects into real images. View details
    Pushing the Boundaries of View Extrapolation with Multiplane Images
    Pratul Srinivasan
    Ravi Ramamoorthi
    Ren Ng
    Computer Vision and Pattern Recognition (CVPR) (2019)
    Preview abstract We explore the problem of view synthesis from a narrow baseline pair of images, and focus on generating high-quality view extrapolations with plausible disocclusions. Our method builds upon prior work in predicting a multiplane image (MPI), which represents scene content as a set of RGBA planes within a reference view frustum and renders novel views by projecting this content into the target viewpoints. We present a theoretical analysis showing how the range of views that can be rendered from an MPI increases linearly with the MPI disparity sampling frequency, as well as a novel MPI prediction procedure that theoretically enables view extrapolations of up to 4 times the lateral viewpoint movement allowed by prior work. Our method ameliorates two specific issues that limit the range of views renderable by prior methods: 1) We expand the range of novel views that can be rendered without depth discretization artifacts by using a 3D convolutional network architecture along with a randomized-resolution training procedure to allow our model to predict MPIs with increased disparity sampling frequency. 2) We reduce the repeated texture artifacts seen in disocclusions by enforcing a constraint that the appearance of hidden content at any depth must be drawn from visible content at or behind that depth. View details
    DeepView: High-quality view synthesis by learned gradient descent
    John Flynn
    Michael Broxton
    Paul Debevec
    Matthew DuVall
    Graham Fyffe
    Ryan Styles Overbeck
    Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
    Preview abstract We present a novel approach to view synthesis using multiplane images (MPIs). Building on recent advances in learned gradient descent, our algorithm generates an MPI from a set of sparse camera viewpoints. The resulting method incorporates occlusion reasoning, improving performance on challenging scene features such as object boundaries, lighting reflections, thin structures, and scenes with high depth complexity. We show that our method achieves high-quality, state-of-the-art results on two datasets: the Kalantari light field dataset, and a new camera array dataset, Spaces. More information is available at the project webpage. View details
    Learning the Depths of Moving People by Watching Frozen People
    Zhengqi Li
    Ce Liu
    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
    Preview abstract We present a method for predicting dense depth in scenarios where both a monocular camera and people in the scene are freely moving. Existing methods for recovering depth for dynamic, non-rigid objects from monocular video impose strong assumptions on the objects' motion and often can recover only a sparse depth. In this paper, we take a data-driven approach and learn human depth priors from a large corpus of data. Specifically, we use a new source of data comprised of thousands of Internet videos in which people imitate mannequins, i.e., people freeze in diverse, natural poses, while a hand-held camera is touring the scene. We then create training data using modern Multi-View Stereo (MVS) methods, and design a model that is applied to dynamic scene at inference time. Our method makes use of motion parallax beyond single view and shows clear advantages over state-of-the-art monocular depth prediction methods. We demonstrate the applicability of our method on real-world sequences captured by a moving hand-held camera, depicting complex human actions. We show various 3D effects such as re-focusing, creating a stereoscopic video from a monocular one, and inserting virtual objects to the scene, all produced using our predicted depth maps. View details
    Layer-structured 3D Scene Inference via View Synthesis
    Shubham Tulsiani
    European Conference on Computer Vision (2018)
    Preview abstract We present an approach to infer a layer-structured 3D representation of a scene from a single input image. This allows us to infer not only the depth of the visible pixels, but also to capture the texture and depth for content in the scene that is not directly visible. We overcome the challenge posed by the lack of direct supervision by instead leveraging a more naturally available multi-view supervisory signal. Our insight is to use view synthesis as a proxy task: we enforce that our representation (inferred from a single image), when rendered from a novel perspective, matches the true observed image. We present a learning framework that operationalizes this insight using a new, differentiable novel view renderer. We provide qualitative and quantitative validation of our approach in two different settings, and demonstrate that we can learn to capture the hidden aspects of a scene. View details
    Stereo Magnification: Learning view synthesis using multiplane images
    Tinghui Zhou
    John Flynn
    Graham Fyffe
    ACM Trans. Graph. (Proc. SIGGRAPH), vol. 37 (2018)
    Preview abstract The view synthesis problem—generating novel views of a scene from known imagery—has garnered recent attention due in part to compelling applications in virtual and augmented reality. In this paper, we explore an intriguing scenario for view synthesis: extrapolating views from imagery captured by narrow-baseline stereo cameras, including dual-lens camera phones and VR cameras. We call this problem stereo magnification, and propose a new learning framework that leverages a new layered representation that we call multiplane images (MPIs), as well as a massive new data source for learning view extrapolation: online videos on YouTube. Using data mined from such videos, we train a deep network that predicts an MPI from an input stereo image pair. This inferred MPI can then be used to synthesize a range of novel views of the scene, including views that extrapolate significantly beyond the input baseline. We show that our method compares favorably with several recent view synthesis methods, and demonstrate applications in magnifying narrow-baseline stereo images. View details
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