We introduce the new setting of open-vocabulary object 6D pose estimation in which a textual prompt is used to specify the object of interest. In contrast to existing approaches in our setting (i) the object of interest is specified solely through the textual prompt (ii) no object model (e.g. CAD or video sequence) is required at inference and (iii) the object is imaged from two RGBD viewpoints of different scenes. To operate in this setting we introduce a novel approach that leverages a Vision-Language Model to segment the object of interest from the scenes and to estimate its relative 6D pose. The key of our approach is a carefully devised strategy to fuse object-level information provided by the prompt with local image features resulting in a feature space that can generalize to novel concepts. We validate our approach on a new benchmark based on two popular datasets REAL275 and Toyota-Light which collectively encompass 34 object instances appearing in four thousand image pairs. The results demonstrate that our approach outperforms both a well-established hand-crafted method and a recent deep learning-based baseline in estimating the relative 6D pose of objects in different scenes. Code and dataset are available at https://jcorsetti.github.io/oryon.
论文检索
输入标题、作者或关键词，从 8,216 篇学术成果中精准定位
We present a new dataset called Real Acoustic Fields (RAF) that captures real acoustic room data from multiple modalities. The dataset includes high-quality and densely captured room impulse response data paired with multi-view images and precise 6DoF pose tracking data for sound emitters and listeners in the rooms. We used this dataset to evaluate existing methods for novel-view acoustic synthesis and impulse response generation which previously relied on synthetic data. In our evaluation we thoroughly assessed existing audio and audio-visual models against multiple criteria and proposed settings to enhance their performance on real-world data. We also conducted experiments to investigate the impact of incorporating visual data (i.e. images and depth) into neural acoustic field models. Additionally we demonstrated the effectiveness of a simple sim2real approach where a model is pre-trained with simulated data and fine-tuned with sparse real-world data resulting in significant improvements in the few-shot learning approach. RAF is the first dataset to provide densely captured room acoustic data making it an ideal resource for researchers working on audio and audio-visual neural acoustic field modeling techniques.
Implicit neural representation has paved the way for new approaches to dynamic scene reconstruction. Nonetheless cutting-edge dynamic neural rendering methods rely heavily on these implicit representations which frequently struggle to capture the intricate details of objects in the scene. Furthermore implicit methods have difficulty achieving real-time rendering in general dynamic scenes limiting their use in a variety of tasks. To address the issues we propose a deformable 3D Gaussians splatting method that reconstructs scenes using 3D Gaussians and learns them in canonical space with a deformation field to model monocular dynamic scenes. We also introduce an annealing smoothing training mechanism with no extra overhead which can mitigate the impact of inaccurate poses on the smoothness of time interpolation tasks in real-world scenes. Through a differential Gaussian rasterizer the deformable 3D Gaussians not only achieve higher rendering quality but also real-time rendering speed. Experiments show that our method outperforms existing methods significantly in terms of both rendering quality and speed making it well-suited for tasks such as novel-view synthesis time interpolation and real-time rendering.
Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot Learning
PDF ↗In this paper we look at cross-domain few-shot classification which presents the challenging task of learning new classes in previously unseen domains with few labelled examples. Existing methods though somewhat effective encounter several limitations which we alleviate through two significant improvements. First we introduce a lightweight parameter-efficient adaptation strategy to address overfitting associated with fine-tuning a large number of parameters on small datasets. This strategy employs a linear transformation of pre-trained features significantly reducing the trainable parameter count. Second we replace the traditional nearest centroid classifier with a discriminative sample-aware loss function enhancing the model's sensitivity to the inter- and intra-class variances within the training set for improved clustering in feature space. Empirical evaluations on the Meta-Dataset benchmark showcase that our approach not only improves accuracy up to 7.7% and 5.3% on previously seen and unseen datasets respectively but also achieves the above performance while being at least 3x more parameter-efficient than existing methods establishing a new state-of-the-art in cross-domain few-shot learning. Our code is available at https://github.com/rashindrie/DIPA.
Probabilistic Speech-Driven 3D Facial Motion Synthesis: New Benchmarks Methods and Applications
PDF ↗We consider the task of animating 3D facial geometry from speech signal. Existing works are primarily deterministic focusing on learning a one-to-one mapping from speech signal to 3D face meshes on small datasets with limited speakers. While these models can achieve high-quality lip articulation for speakers in the training set they are unable to capture the full and diverse distribution of 3D facial motions that accompany speech in the real world. Importantly the relationship between speech and facial motion is one-to-many containing both inter-speaker and intra-speaker variations and necessitating a probabilistic approach. In this paper we identify and address key challenges that have so far limited the development of probabilistic models: lack of datasets and metrics that are suitable for training and evaluating them as well as the difficulty of designing a model that generates diverse results while remaining faithful to a strong conditioning signal as speech. We first propose large-scale benchmark datasets and metrics suitable for probabilistic modeling. Then we demonstrate a probabilistic model that achieves both diversity and fidelity to speech outperforming other methods across the proposed benchmarks. Finally we showcase useful applications of probabilistic models trained on these large-scale datasets: we can generate diverse speech-driven 3D facial motion that matches unseen speaker styles extracted from reference clips; and our synthetic meshes can be used to improve the performance of downstream audio-visual models.
Recent advances in neural rendering have improved both training and rendering times by orders of magnitude. While these methods demonstrate state-of-the-art quality and speed they are designed for photogrammetry of static scenes and do not generalize well to freely moving humans in the environment. In this work we introduce Human Gaussian Splats (HUGS) that represents an animatable human together with the scene using 3D Gaussian Splatting (3DGS). Our method takes only a monocular video with a small number of (50-100) frames and it automatically learns to disentangle the static scene and a fully animatable human avatar within 30 minutes. We utilize the SMPL body model to initialize the human Gaussians. To capture details that are not modeled by SMPL (e.g cloth hairs) we allow the 3D Gaussians to deviate from the human body model. Utilizing 3D Gaussians for animated humans brings new challenges including the artifacts created when articulating the Gaussians. We propose to jointly optimize the linear blend skinning weights to coordinate the movements of individual Gaussians during animation. Our approach enables novel-pose synthesis of human and novel view synthesis of both the human and the scene. We achieve state-of-the-art rendering quality with a rendering speed of 60 FPS while being 100x faster to train over previous work.
The You Only Look Once (YOLO) series of detectors have established themselves as efficient and practical tools. However their reliance on predefined and trained object categories limits their applicability in open scenarios. Addressing this limitation we introduce YOLO-World an innovative approach that enhances YOLO with open-vocabulary detection capabilities through vision-language modeling and pre-training on large-scale datasets. Specifically we propose a new Re-parameterizable Vision-Language Path Aggregation Network (RepVL-PAN) and region-text contrastive loss to facilitate the interaction between visual and linguistic information. Our method excels in detecting a wide range of objects in a zero-shot manner with high efficiency. On the challenging LVIS dataset YOLO-World achieves 35.4 AP with 52.0 FPS on V100 which outperforms many state-of-the-art methods in terms of both accuracy and speed. Furthermore the fine-tuned YOLO-World achieves remarkable performance on several downstream tasks including object detection and open-vocabulary instance segmentation. Code and models are available at https://github.com/AILab-CVC/YOLO-World
Multimodal large language models (MLLMs) have recently achieved impressive general-purpose vision-language capabilities through visual instruction tuning. However current MLLMs primarily focus on image-level or box-level understanding falling short in achieving fine-grained vision-language alignment at pixel level. Besides the lack of mask-based instruction data limits their advancements. In this paper we propose Osprey a mask-text instruction tuning approach to extend MLLMs by incorporating fine-grained mask regions into language instruction aiming at achieving pixel-wise visual understanding. To achieve this goal we first meticulously curate a mask-based region-text dataset with 724K samples and then design a vision-language model by injecting pixel-level representation into LLM. Specifically Osprey adopts a convolutional CLIP backbone as the vision encoder and employs a mask-aware visual extractor to extract precise visual mask features from high resolution input. Experimental results demonstrate Osprey's superiority in various region understanding tasks showcasing its new capability for pixel-level instruction tuning. In particular Osprey can be integrated with Segment Anything Model (SAM) seamlessly to obtain multi-granularity semantics. The source code dataset and demo can be found at https://github.com/CircleRadon/Osprey.
Controllable generation of 3D assets is important for many practical applications like content creation in movies games and engineering as well as in AR/VR. Recently diffusion models have shown remarkable results in generation quality of 3D objects. However none of the existing models enable disentangled generation to control the shape and appearance separately. For the first time we present a suitable representation for 3D diffusion models to enable such disentanglement by introducing a hybrid point cloud and neural radiance field approach. We model a diffusion process over point positions jointly with a high-dimensional feature space for a local density and radiance decoder. While the point positions represent the coarse shape of the object the point features allow modeling the geometry and appearance details. This disentanglement enables us to sample both independently and therefore to control both separately. Our approach sets a new state of the art in generation compared to previous disentanglement-capable methods by reduced FID scores of 30-90% and is on-par with other non-disentanglement-capable state-of-the art methods.
GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis
PDF ↗We present a new approach termed GPS-Gaussian for synthesizing novel views of a character in a real-time manner. The proposed method enables 2K-resolution rendering under a sparse-view camera setting. Unlike the original Gaussian Splatting or neural implicit rendering methods that necessitate per-subject optimizations we introduce Gaussian parameter maps defined on the source views and regress directly Gaussian Splatting properties for instant novel view synthesis without any fine-tuning or optimization. To this end we train our Gaussian parameter regression module on a large amount of human scan data jointly with a depth estimation module to lift 2D parameter maps to 3D space. The proposed framework is fully differentiable and experiments on several datasets demonstrate that our method outperforms state-of-the-art methods while achieving an exceeding rendering speed.
The anatomical structure detection of fetal cardiac views is crucial for diagnosing fetal congenital heart disease. In practice there is a large domain gap between different hospitals' data such as the variable data quality due to differences in acquisition equipment. In addition accurate annotation information provided by obstetrician experts is always very costly or even unavailable. This study explores the unsupervised domain adaptive fetal cardiac structure detection issue. Existing unsupervised domain adaptive object detection (UDAOD) approaches mainly focus on detecting objects in natural scenes such as Foggy Cityscapes where the structural relationships of natural scenes are uncertain. Unlike all previous UDAOD scenarios we first collected a Fetal Cardiac Structure dataset from two hospital centers called FCS and proposed a multi-matching UDA approach (M3-UDA) including Histogram Matching (HM) Sub-structure Matching (SM) and Global-structure Matching (GM) to better transfer the topological knowledge of anatomical structure for UDA detection in medical scenarios. HM mitigates the domain gap between the source and target caused by pixel transformation. SM fuses the different angle information of the sub-structure to obtain the local topological knowledge for bridging the domain gap of the internal sub-structure. GM is designed to align the global topological knowledge of the whole organ from the source and target domain. Extensive experiments on our collected FCS and CardiacUDA and experimental results show that M3-UDA outperforms existing UDAOD studies significantly. All datasets and source code are available at : https://github.com/xmed-lab/M3-UDA
Generating Audio Description (AD) for movies is a challenging task that requires fine-grained visual understanding and an awareness of the characters and their names. Currently visual language models for AD generation are limited by a lack of suitable training data and also their evaluation is hampered by using performance measures not specialized to the AD domain. In this paper we make three contributions: (i) We propose two approaches for constructing AD datasets with aligned video data and build training and evaluation datasets using these. These datasets will be publicly released; (ii) We develop a Q-former-based architecture which ingests raw video and generates AD using frozen pre-trained visual encoders and large language models; and (iii) We provide new evaluation metrics to benchmark AD quality that are well matched to human performance. Taken together we improve the state of the art on AD generation.
While neural rendering has led to impressive advances in scene reconstruction and novel view synthesis it relies heavily on accurately pre-computed camera poses. To relax this constraint multiple efforts have been made to train Neural Radiance Fields (NeRFs) without pre-processed camera poses. However the implicit representations of NeRFs provide extra challenges to optimize the 3D structure and camera poses at the same time. On the other hand the recently proposed 3D Gaussian Splatting provides new opportunities given its explicit point cloud representations. This paper leverages both the explicit geometric representation and the continuity of the input video stream to perform novel view synthesis without any SfM preprocessing. We process the input frames in a sequential manner and progressively grow the 3D Gaussians set by taking one input frame at a time without the need to pre-compute the camera poses. Our method significantly improves over previous approaches in view synthesis and camera pose estimation under large motion changes. Our project page is: https: //oasisyang.github.io/colmap-free-3dgs.
Continual learning methods are known to suffer from catastrophic forgetting a phenomenon that is particularly hard to counter for methods that do not store exemplars of previous tasks. Therefore to reduce potential drift in the feature extractor existing exemplar-free methods are typically evaluated in settings where the first task is significantly larger than subsequent tasks. Their performance drops drastically in more challenging settings starting with a smaller first task. To address this problem of feature drift estimation for exemplar-free methods we propose to adversarially perturb the current samples such that their embeddings are close to the old class prototypes in the old model embedding space. We then estimate the drift in the embedding space from the old to the new model using the perturbed images and compensate the prototypes accordingly. We exploit the fact that adversarial samples are transferable from the old to the new feature space in a continual learning setting. The generation of these images is simple and computationally cheap. We demonstrate in our experiments that the proposed approach better tracks the movement of prototypes in embedding space and outperforms existing methods on several standard continual learning benchmarks as well as on fine-grained datasets. Code is available at https://github.com/dipamgoswami/ADC.
Traditional approaches in physics-based motion generation centered around imitation learning and reward shaping often struggle to adapt to new scenarios. To tackle this limitation we propose AnySkill a novel hierarchical method that learns physically plausible interactions following open-vocabulary instructions. Our approach begins by developing a set of atomic actions via a low-level controller trained via imitation learning. Upon receiving an open-vocabulary textual instruction AnySkill employs a high-level policy that selects and integrates these atomic actions to maximize the CLIP similarity between the agent's rendered images and the text. An important feature of our method is the use of image-based rewards for the high-level policy which allows the agent to learn interactions with objects without manual reward engineering. We demonstrate AnySkill's capability to generate realistic and natural motion sequences in response to unseen instructions of varying lengths marking it the first method capable of open-vocabulary physical skill learning for interactive humanoid agents.
The goal of multi-task learning is to learn diverse tasks within a single unified network. As each task has its own unique objective function conflicts emerge during training resulting in negative transfer among them. Earlier research identified these conflicting gradients in shared parameters between tasks and attempted to realign them in the same direction. However we prove that such optimization strategies lead to sub-optimal Pareto solutions due to their inability to accurately determine the individual contributions of each parameter across various tasks. In this paper we propose the concept of task priority to evaluate parameter contributions across different tasks. To learn task priority we identify the type of connections related to links between parameters influenced by task-specific losses during backpropagation. The strength of connections is gauged by the magnitude of parameters to determine task priority. Based on these we present a new method named connection strength-based optimization for multi-task learning which consists of two phases. The first phase learns the task priority within the network while the second phase modifies the gradients while upholding this priority. This ultimately leads to finding new Pareto optimal solutions for multiple tasks. Through extensive experiments we show that our approach greatly enhances multi-task performance in comparison to earlier gradient manipulation methods.
Recent progress in single-image 3D generation highlights the importance of multi-view coherency leveraging 3D priors from large-scale diffusion models pretrained on Internet-scale images. However the aspect of novel-view diversity remains underexplored within the research landscape due to the ambiguity in converting a 2D image into 3D content where numerous potential shapes can emerge. Here we aim to address this research gap by simultaneously addressing both consistency and diversity. Yet striking a balance between these two aspects poses a considerable challenge due to their inherent trade-offs. This work introduces HarmonyView a simple yet effective diffusion sampling technique adept at decomposing two intricate aspects in single-image 3D generation: consistency and diversity. This approach paves the way for a more nuanced exploration of the two critical dimensions within the sampling process. Moreover we propose a new evaluation metric based on CLIP image and text encoders to comprehensively assess the diversity of the generated views which closely aligns with human evaluators' judgments. In experiments HarmonyView achieves a harmonious balance demonstrating a win-win scenario in both consistency and diversity.
We present S4Former a novel approach to training Vision Transformers for Semi-Supervised Semantic Segmentation (S4). At its core S4Former employs a Vision Transformer within a classic teacher-student framework and then leverages three novel technical ingredients: PatchShuffle as a parameter-free perturbation technique Patch-Adaptive Self-Attention (PASA) as a fine-grained feature modulation method and the innovative Negative Class Ranking (NCR) regularization loss. Based on these regularization modules aligned with Transformer-specific characteristics across the image input feature and output dimensions S4Former exploits the Transformer's ability to capture and differentiate consistent global contextual information in unlabeled images. Overall S4Former not only defines a new state of the art in S4 but also maintains a streamlined and scalable architecture. Being readily compatible with existing frameworks S4Former achieves strong improvements (up to 4.9%) on benchmarks like Pascal VOC 2012 COCO and Cityscapes with varying numbers of labeled data. The code is at https://github.com/JoyHuYY1412/S4Former.
Low-shot counters estimate the number of objects corresponding to a selected category based on only few or no exemplars annotated in the image. The current state-of-the-art estimates the total counts as the sum over the object location density map but do not provide object locations and sizes which are crucial for many applications. This is addressed by detection-based counters which however fall behind in the total count accuracy. Furthermore both approaches tend to overestimate the counts in the presence of other object classes due to many false positives. We propose DAVE a low-shot counter based on a detect-and-verify paradigm that avoids the aforementioned issues by first generating a high-recall detection set and then verifying the detections to identify and remove the outliers.This jointly increases the recall and precision leading to accurate counts. DAVE outperforms the top density-based counters by ?20% in the total count MAE it outperforms the most recent detection-based counter by ?20% in detection quality and sets a new state-of-the-art in zero-shot as well as text-prompt-based counting.
Reconstructing outdoor 3D scenes from temporal observations is a challenge that recent work on neural fields has offered a new avenue for. However existing methods that recover scene properties such as geometry appearance or radiance solely from RGB captures often fail when handling poorly-lit or texture-deficient regions. Similarly recovering scenes with scanning lidar sensors is also difficult due to their low angular sampling rate which makes recovering expansive real-world scenes difficult. Tackling these gaps we introduce Gated Fields - a neural scene reconstruction method that utilizes active gated video sequences. To this end we propose a neural rendering approach that seamlessly incorporates time-gated capture and illumination. Our method exploits the intrinsic depth cues in the gated videos achieving precise and dense geometry reconstruction irrespective of ambient illumination conditions. We validate the method across day and night scenarios and find that Gated Fields compares favorably to RGB and LiDAR reconstruction methods