We present FoundationPose a unified foundation model for 6D object pose estimation and tracking supporting both model-based and model-free setups. Our approach can be instantly applied at test-time to a novel object without finetuning as long as its CAD model is given or a small number of reference images are captured. Thanks to the unified framework the downstream pose estimation modules are the same in both setups with a neural implicit representation used for efficient novel view synthesis when no CAD model is available. Strong generalizability is achieved via large-scale synthetic training aided by a large language model (LLM) a novel transformer-based architecture and contrastive learning formulation. Extensive evaluation on multiple public datasets involving challenging scenarios and objects indicate our unified approach outperforms existing methods specialized for each task by a large margin. In addition it even achieves comparable results to instance-level methods despite the reduced assumptions. Project page: https://nvlabs.github.io/FoundationPose/
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What When and Where? Self-Supervised Spatio-Temporal Grounding in Untrimmed Multi-Action Videos from Narrated Instructions
PDF ↗Spatio-temporal grounding describes the task of localizing events in space and time e.g. in video data based on verbal descriptions only. Models for this task are usually trained with human-annotated sentences and bounding box supervision. This work addresses this task from a multimodal supervision perspective proposing a framework for spatio-temporal action grounding trained on loose video and subtitle supervision only without human annotation. To this end we combine local representation learning which focuses on leveraging fine-grained spatial information with a global representation encoding that captures higher-level representations and incorporates both in a joint approach. To evaluate this challenging task in a real-life setting a new benchmark dataset is proposed providing dense spatio-temporal grounding annotations in long untrimmed multi-action instructional videos for over 5K events. We evaluate the proposed approach and other methods on the proposed and standard downstream tasks showing that our method improves over current baselines in various settings including spatial temporal and untrimmed multi-action spatio-temporal grounding.
Building accurate maps is a key building block to enable reliable localization planning and navigation of autonomous vehicles. We propose a novel approach for building accurate 3D maps of dynamic environments utilizing a sequence of LiDAR scans. To this end we propose encoding the 4D scene into a novel spatio-temporal implicit neural map representation by fitting a time-dependent truncated signed distance function to each point. Using our representation we can extract the static map by filtering the dynamic parts. Our neural representation is based on sparse feature grids a globally shared decoder and time-dependent basis functions which can be jointly optimized in an unsupervised fashion. To learn this representation from a sequence of LiDAR scans we design a simple yet efficient loss function to supervise the map optimization in a piecewise way. We evaluate our approach on various scenes containing moving objects in terms of the reconstruction quality of static maps and the segmentation of dynamic point clouds. The experimental results demonstrate that our method is capable of removing the dynamic part of the input point clouds while reconstructing accurate and complete large-scale 3D maps outperforming several state-of-the-art methods for static map generation and scene reconstruction.
In this paper we suggest a new novel method to understand complex semantic structures through long video inputs. Conventional methods for understanding videos have been focused on short-term clips and trained to get visual representations for the short clips using convolutional neural networks or transformer architectures. However most real-world videos are composed of long videos ranging from minutes to hours therefore it essentially brings limitations to understanding the overall semantic structures of the long videos by dividing them into small clips and learning the representations of them. We suggest a new algorithm to learn the multi-granular semantic structures of videos by defining spatiotemporal high-order relationships among object-based representations as semantic units. The proposed method includes a new transformer architecture capable of learning spatiotemporal graphs and a compositional learning method to learn disentangled features for each semantic unit. Using the suggested method we resolve the challenging video task which is compositional generalization understanding of unseen videos. In experiments we demonstrate new state-of-the-art performances for two challenging video datasets.
We present Image Sculpting a new framework for editing 2D images by incorporating tools from 3D geometry and graphics. This approach differs markedly from existing methods which are confined to 2D spaces and typically rely on textual instructions leading to ambiguity and limited control. Image Sculpting converts 2D objects into 3D enabling direct interaction with their 3D geometry. Post-editing these objects are re-rendered into 2D merging into the original image to produce high-fidelity results through a coarse-to-fine enhancement process. The framework supports precise quantifiable and physically-plausible editing options such as pose editing rotation translation 3D composition carving and serial addition. It marks an initial step towards combining the creative freedom of generative models with the precision of graphics pipelines.
Referring image segmentation (RIS) aims to segment the target referent described by natural language. Recently large-scale pre-trained models e.g. CLIP and SAM have been successfully applied in many downstream tasks but they are not well adapted to RIS task due to inter-task differences. In this paper we propose a new prompt-driven framework named Prompt-RIS which bridges CLIP and SAM end-to-end and transfers their rich knowledge and powerful capabilities to RIS task through prompt learning. To adapt CLIP to pixel-level task we first propose a Cross-Modal Prompting method which acquires more comprehensive vision-language interaction and fine-grained text-to-pixel alignment by performing bidirectional prompting. Then the prompt-tuned CLIP generates masks points and text prompts for SAM to generate more accurate mask predictions. Moreover we further propose Instance Contrastive Learning to improve the model's discriminability to different instances and robustness to diverse languages describing the same instance. Extensive experiments demonstrate that the performance of our method outperforms the state-of-the-art methods consistently in both general and open-vocabulary settings.
We train a set of open text-to-image (T2I) diffusion models on a dataset of curated Creative-Commons-licensed (CC) images which yields models that are competitive with Stable Diffusion 2 (SD2). This task presents two challenges: (1) high-resolution CC images lack the captions necessary to train T2I models; (2) CC images are relatively scarce. To address these challenges we use an intuitive transfer learning technique to produce a set of high-quality synthetic captions paired with our assembled CC images. We then develop a data- and compute-efficient training recipe that requires as little as 3% of the LAION data (i.e. roughly 70 million examples) needed to train existing SD2 models but obtains the same quality. These results indicate that we have a sufficient number of CC images (also roughly 70 million) for training high-quality models. Our recipe also implements a variety of optimizations that achieve 2.71x training speed-ups enabling rapid model iteration. We leverage this recipe to train several high-quality T2I mod- els which we dub the CommonCanvas family. Our largest model achieves comparable performance to SD2 on human evaluation even though we use a synthetically captioned CC-image dataset that is only <3% the size of LAION for training. We release our models data and code on GitHub.
The surge in multi-modal data has propelled cross-modal matching to the forefront of research interest. However the challenge lies in the laborious and expensive process of curating a large and accurately matched multimodal dataset. Commonly sourced from the Internet these datasets often suffer from a significant presence of mismatched data impairing the performance of matching models. To address this problem we introduce a novel regularization approach named Equivariant Similarity Consistency (ESC) which can facilitate robust clean and noisy data separation and improve the training for cross-modal matching. Intuitively our method posits that the semantic variations caused by image changes should be proportional to those caused by text changes for any two matched samples. Accordingly we first calculate the ESC by comparing image and text semantic variations between a set of elaborated anchor points and other undivided training data. Then pairs with high ESC are filtered out as noisy correspondence pairs. We implement our method by combining the ESC with a traditional hinge-based triplet loss. Extensive experiments on three widely used datasets including Flickr30K MS-COCO and Conceptual Captions verify the effectiveness of our method.
A significant challenge facing current optical flow methods is the difficulty in generalizing them well to the real world. This is mainly due to the lack of large-scale real-world datasets and existing self-supervised methods are limited by indirect loss and occlusions resulting in fuzzy outcomes. To address this challenge we introduce a novel optical flow training framework: automatic data factory (ADF). ADF only requires RGB images as input to effectively train the optical flow network on the target data domain. Specifically we use advanced NeRF technology to reconstruct scenes from photo groups collected by a monocular camera and then calculate optical flow labels between camera pose pairs based on the rendering results. To eliminate erroneous labels caused by defects in the scene reconstructed by NeRF we screened the generated labels from multiple aspects such as optical flow matching accuracy radiation field confidence and depth consistency. The filtered labels can be directly used for network supervision. Experimentally the generalization ability of ADF on KITTI surpasses existing self-supervised optical flow and monocular scene flow algorithms. In addition ADF achieves impressive results in real-world zero-point generalization evaluations and surpasses most supervised methods.
A simple yet effective method for occlusion-robust 3D human mesh reconstruction from a single image is presented in this paper. Although many recent studies have shown the remarkable improvement in human mesh reconstruction it is still difficult to generate accurate meshes when person-to-person occlusion occurs due to the ambiguity of who a body part belongs to. To address this problem we propose an instance-aware contrastive learning scheme. Specifically joint features belonging to the target human are trained to be proximate with the anchor feature (i.e. feature extracted from the body center position). On the other hand anchor features of different human instances are forced to be far apart so that joint features of each person can be clearly distinguished from others. By interpreting the joint possession based on such contrastive learning scheme the proposed method easily understands the spatial occupancy of body parts for each person in a given image thus can reconstruct reliable human meshes even with severely overlapped cases between multiple persons. Experimental results on benchmark datasets demonstrate the robustness of the proposed method compared to previous approaches under person-to-person occlusions. The code and model are publicly available at: https://github.com/DCVL-3D/InstanceHMR_release.
We present SuperNormal a fast high-fidelity approach to multi-view 3D reconstruction using surface normal maps. With a few minutes SuperNormal produces detailed surfaces on par with 3D scanners. We harness volume rendering to optimize a neural signed distance function (SDF) powered by multi-resolution hash encoding. To accelerate training we propose directional finite difference and patchbased ray marching to approximate the SDF gradients numerically. While not compromising reconstruction quality this strategy is nearly twice as efficient as analytical gradients and about three times faster than axis-aligned finite difference. Experiments on the benchmark dataset demonstrate the superiority of SuperNormal in efficiency and accuracy compared to existing multi-view photometric stereo methods. On our captured objects SuperNormal produces more fine-grained geometry than recent neural 3D reconstruction methods. Our code is available at https://github.com/CyberAgentAILab/SuperNormal.git.
Can we capture shape and reflectance in stealth? Such capability would be valuable for many application domains in vision xR robotics and HCI. We introduce structured polarization for invisible depth and reflectance sensing (SPIDeRS) the first depth and reflectance sensing method using patterns of polarized light. The key idea is to modulate the angle of linear polarization (AoLP) of projected light at each pixel. The use of polarization makes it invisible and lets us recover not only depth but also directly surface normals and even reflectance. We implement SPIDeRS with a liquid crystal spatial light modulator (SLM) and a polarimetric camera. We derive a novel method for robustly extracting the projected structured polarization pattern from the polarimetric object appearance. We evaluate the effectiveness of SPIDeRS by applying it to a number of real-world objects. The results show that our method successfully reconstructs object shapes of various materials and is robust to diffuse reflection and ambient light. We also demonstrate relighting using recovered surface normals and reflectance. We believe SPIDeRS opens a new avenue of polarization use in visual sensing.
Reconstructing High Dynamic Range (HDR) video from image sequences captured with alternating exposures is challenging especially in the presence of large camera or object motion. Existing methods typically align low dynamic range sequences using optical flow or attention mechanism for deghosting. However they often struggle to handle large complex motions and are computationally expensive. To address these challenges we propose a robust and efficient flow estimator tailored for real-time HDR video reconstruction named HDRFlow. HDRFlow has three novel designs: an HDR-domain alignment loss (HALoss) an efficient flow network with a multi-size large kernel (MLK) and a new HDR flow training scheme. The HALoss supervises our flow network to learn an HDR-oriented flow for accurate alignment in saturated and dark regions. The MLK can effectively model large motions at a negligible cost. In addition we incorporate synthetic data Sintel into our training dataset utilizing both its provided forward flow and backward flow generated by us to supervise our flow network enhancing our performance in large motion regions. Extensive experiments demonstrate that our HDRFlow outperforms previous methods on standard benchmarks. To the best of our knowledge HDRFlow is the first real-time HDR video reconstruction method for video sequences captured with alternating exposures capable of processing 720p resolution inputs at 25ms.
Recently efficient Vision Transformers have shown great performance with low latency on resource-constrained devices. Conventionally they use 4x4 patch embeddings and a 4-stage structure at the macro level while utilizing sophisticated attention with multi-head configuration at the micro level. This paper aims to address computational redundancy at all design levels in a memory-efficient manner. We discover that using larger-stride patchify stem not only reduces memory access costs but also achieves competitive performance by leveraging token representations with reduced spatial redundancy from the early stages. Furthermore our preliminary analyses suggest that attention layers in the early stages can be substituted with convolutions and several attention heads in the latter stages are computationally redundant. To handle this we introduce a single-head attention module that inherently prevents head redundancy and simultaneously boosts accuracy by parallelly combining global and local information. Building upon our solutions we introduce SHViT a Single-Head Vision Transformer that obtains the state-of-the-art speed-accuracy tradeoff. For example on ImageNet-1k our SHViT-S4 is 3.3x 8.1x and 2.4x faster than MobileViTv2x1.0 on GPU CPU and iPhone12 mobile device respectively while being 1.3% more accurate. For object detection and instance segmentation on MS COCO using Mask-RCNN head our model achieves performance comparable to FastViT-SA12 while exhibiting 3.8x and 2.0x lower backbone latency on GPU and mobile device respectively.
Synchronization issues between audio and video are one of the most disturbing quality defects in film production and live broadcasting. Even a discrepancy as short as 45 millisecond can degrade the viewer's experience enough to warrant manual quality checks over entire movies. In this paper we study the automatic discovery of such issues. Specifically we focus on the alignment of lip movements with spoken words targeting realistic production scenarios which can include background noise and music intricate head poses excessive makeup or scenes with multiple individuals where the speaker is unknown. Our model's robustness also extends to various media specifications including different video frame rates and audio sample rates. To address these challenges we present a model fully based on transformers that encodes face crops or full video frames and raw audio using timestamp information identifies the speaker and provides highly accurate synchronization predictions much faster than previous methods.
Your Student is Better Than Expected: Adaptive Teacher-Student Collaboration for Text-Conditional Diffusion Models
PDF ↗Knowledge distillation methods have recently shown to be a promising direction to speedup the synthesis of large-scale diffusion models by requiring only a few inference steps. While several powerful distillation methods were recently proposed the overall quality of student samples is typically lower compared to the teacher ones which hinders their practical usage. In this work we investigate the relative quality of samples produced by the teacher text-to-image diffusion model and its distilled student version. As our main empirical finding we discover that a noticeable portion of student samples exhibit superior fidelity compared to the teacher ones despite the approximate nature of the student. Based on this finding we propose an adaptive collaboration between student and teacher diffusion models for effective text-to-image synthesis. Specifically the distilled model produces an initial image sample and then an oracle decides whether it needs further improvements with the teacher model. Extensive experiments demonstrate that the designed pipeline surpasses state-of-the-art text-to-image alternatives for various inference budgets in terms of human preference. Furthermore the proposed approach can be naturally used in popular applications such as text-guided image editing and controllable generation.
We introduce Gaussian-Flow a novel point-based approach for fast dynamic scene reconstruction and real-time rendering from both multi-view and monocular videos. In contrast to the prevalent NeRF-based approaches hampered by slow training and rendering speeds our approach harnesses recent advancements in point-based 3D Gaussian Splatting (3DGS). Specifically a novel Dual-Domain Deformation Model (DDDM) is proposed to explicitly model attribute deformations of each Gaussian point where the time-dependent residual of each attribute is captured by a polynomial fitting in the time domain and a Fourier series fitting in the frequency domain. The proposed DDDM is capable of modeling complex scene deformations across long video footage eliminating the need for training separate 3DGS for each frame or introducing an additional implicit neural field to model 3D dynamics. Moreover the explicit deformation modeling for discretized Gaussian points ensures ultra-fast training and rendering of a 4D scene which is comparable to the original 3DGS designed for static 3D reconstruction. Our proposed approach showcases a substantial efficiency improvement achieving a 5xfaster training speed compared to the per-frame 3DGS modeling. In addition quantitative results demonstrate that the proposed Gaussian-Flow significantly outperforms previous leading methods in novel view rendering quality.
The advances in the Neural Radiance Fields (NeRF) research offer extensive applications in diverse domains but protecting their copyrights has not yet been researched in depth. Recently NeRF watermarking has been considered one of the pivotal solutions for safely deploying NeRF-based 3D representations. However existing methods are designed to apply only to implicit or explicit NeRF representations. In this work we introduce an innovative watermarking method that can be employed in both representations of NeRF. This is achieved by fine-tuning NeRF to embed binary messages in the rendering process. In detail we propose utilizing the discrete wavelet transform in the NeRF space for watermarking. Furthermore we adopt a deferred back-propagation technique and introduce a combination with the patch-wise loss to improve rendering quality and bit accuracy with minimum trade-offs. We evaluate our method in three different aspects: capacity invisibility and robustness of the embedded watermarks in the 2D-rendered images. Our method achieves state-of-the-art performance with faster training speed over the compared state-of-the-art methods. Project page: https://kuai-lab.github.io/cvpr2024waterf/
Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers
PDF ↗Recent advancements in 3D reconstruction from single images have been driven by the evolution of generative models. Prominent among these are methods based on Score Distillation Sampling (SDS) and the adaptation of diffusion models in the 3D domain. Despite their progress these techniques often face limitations due to slow optimization or rendering processes leading to extensive training and optimization times. In this paper we introduce a novel approach for single-view reconstruction that efficiently generates a 3D model from a single image via feed-forward inference. Our method utilizes two transformer-based networks namely a point decoder and a triplane decoder to reconstruct 3D objects using a hybrid Triplane-Gaussian intermediate representation. This hybrid representation strikes a balance achieving a faster rendering speed compared to implicit representations while simultaneously delivering superior rendering quality than explicit representations. The point decoder is designed for generating point clouds from single images offering an explicit representation which is then utilized by the triplane decoder to query Gaussian features for each point. This design choice addresses the challenges associated with directly regressing explicit 3D Gaussian attributes characterized by their non-structural nature. Subsequently the 3D Gaussians are decoded by an MLP to enable rapid rendering through splatting. Both decoders are built upon a scalable transformer-based architecture and have been efficiently trained on large-scale 3D datasets. The evaluations conducted on both synthetic datasets and real-world images demonstrate that our method not only achieves higher quality but also ensures a faster runtime in comparison to previous state-of-the-art techniques. Please see our project page at https://zouzx.github.io/TriplaneGaussian/
The systematic evaluation and understanding of computer vision models under varying conditions require large amounts of data with comprehensive and customized labels which real-world vision datasets rarely satisfy. While current synthetic data generators offer a promising alternative particularly for embodied AI tasks they often fall short for computer vision tasks due to low asset and rendering quality limited diversity and unrealistic physical properties. We introduce the BEHAVIOR Vision Suite (BVS) a set of tools and assets to generate fully customized synthetic data for systematic evaluation of computer vision models based on the newly developed embodied AI benchmark BEHAVIOR-1K. BVS supports a large number of adjustable parameters at the scene level (e.g. lighting object placement) the object level (e.g. joint configuration attributes such as "filled" and "folded") and the camera level (e.g. field of view focal length). Researchers can arbitrarily vary these parameters during data generation to perform controlled experiments. We showcase three example application scenarios: systematically evaluating the robustness of models across different continuous axes of domain shift evaluating scene understanding models on the same set of images and training and evaluating simulation-to-real transfer for a novel vision task: unary and binary state prediction. Project website: https://behavior-vision-suite.github.io/