CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
We propose ExtraNeRF a novel method for extrapolating the range of views handled by a Neural Radiance Field (NeRF). Our main idea is to leverage NeRFs to model scene-specific fine-grained details while capitalizing on diffusion models to extrapolate beyond our observed data. A key ingredient is to track visibility to determine what portions of the scene have not been observed and focus on reconstructing those regions consistently with diffusion models. Our primary contributions include a visibility-aware diffusion-based inpainting module that is fine-tuned on the input imagery yielding an initial NeRF with moderate quality (often blurry) inpainted regions followed by a second diffusion model trained on the input imagery to consistently enhance notably sharpen the inpainted imagery from the first pass. We demonstrate high-quality results extrapolating beyond a small number of (typically six or fewer) input views effectively outpainting the NeRF as well as inpainting newly disoccluded regions inside the original viewing volume. We compare with related work both quantitatively and qualitatively and show significant gains over prior art.
Plug-and-play algorithms constitute a popular framework for solving inverse imaging problems that rely on the implicit definition of an image prior via a denoiser. These algorithms can leverage powerful pre-trained denoisers to solve a wide range of imaging tasks circumventing the necessity to train models on a per-task basis. Unfortunately plug-and-play methods often show unstable behaviors hampering their promise of versatility and leading to suboptimal quality of reconstructed images. In this work we show that enforcing equivariance to certain groups of transformations (rotations reflections and/or translations) on the denoiser strongly improves the stability of the algorithm as well as its reconstruction quality. We provide a theoretical analysis that illustrates the role of equivariance on better performance and stability. We present a simple algorithm that enforces equivariance on any existing denoiser by simply applying a random transformation to the input of the denoiser and the inverse transformation to the output at each iteration of the algorithm. Experiments on multiple imaging modalities and denoising networks show that the equivariant plug-and-play algorithm improves both the reconstruction performance and the stability compared to their non-equivariant counterparts.
Existing open-vocabulary image segmentation methods require a fine-tuning step on mask labels and/or image-text datasets. Mask labels are labor-intensive which limits the number of categories in segmentation datasets. Consequently the vocabulary capacity of pre-trained VLMs is severely reduced after fine-tuning. However without fine-tuning VLMs trained under weak image-text supervision tend to make suboptimal mask predictions. To alleviate these issues we introduce a novel recurrent framework that progressively filters out irrelevant texts and enhances mask quality without training efforts. The recurrent unit is a two-stage segmenter built upon a frozen VLM. Thus our model retains the VLM's broad vocabulary space and equips it with segmentation ability. Experiments show that our method outperforms not only the training-free counterparts but also those fine-tuned with millions of data samples and sets the new state-of-the-art records for both zero-shot semantic and referring segmentation. Concretely we improve the current record by 28.8 16.0 and 6.9 mIoU on Pascal VOC COCO Object and Pascal Context.
In a recent strongly emergent literature on few-shot CLIP adaptation Linear Probe (LP) has been often reported as a weak baseline. This has motivated intensive research building convoluted prompt learning or feature adaptation strategies. In this work we propose and examine from convex-optimization perspectives a generalization of the standard LP baseline in which the linear classifier weights are learnable functions of the text embedding with class-wise multipliers blending image and text knowledge. As our objective function depends on two types of variables i.e. the class visual prototypes and the learnable blending parameters we propose a computationally efficient block coordinate Majorize-Minimize (MM) descent algorithm. In our full-batch MM optimizer which we coin LP++ step sizes are implicit unlike standard gradient descent practices where learning rates are intensively searched over validation sets. By examining the mathematical properties of our loss (e.g. Lipschitz gradient continuity) we build majorizing functions yielding data-driven learning rates and derive approximations of the loss's minima which provide data-informed initialization of the variables. Our image-language objective function along with these non-trivial optimization insights and ingredients yields surprisingly highly competitive few-shot CLIP performances.
Lipschitz gradient continuity) we build majorizing functions yielding data-driven learning rates and derive approximations of the loss's minima which provide data-informed initialization of the variables. Our image-language objective function along with these non-trivial optimization insights and ingredients yields surprisingly highly competitive few-shot CLIP performances. Furthermore LP++ operates in black-box relaxes intensive validation searches for the optimization hyper-parameters and runs orders-of-magnitudes faster than state-of-the-art few-shot CLIP adaptation methods. Our code is available at: https://github.com/FereshteShakeri/FewShot-CLIP-Strong-Baseline.git.
Generalized Category Discovery (GCD) is a pragmatic and challenging open-world task which endeavors to cluster unlabeled samples from both novel and old classes leveraging some labeled data of old classes. Given that knowledge learned from old classes is not fully transferable to new classes and that novel categories are fully unlabeled GCD inherently faces intractable problems including imbalanced classification performance and inconsistent confidence between old and new classes especially in the low-labeling regime. Hence some annotations of new classes are deemed necessary. However labeling new classes is extremely costly. To address this issue we take the spirit of active learning and propose a new setting called Active Generalized Category Discovery (AGCD). The goal is to improve the performance of GCD by actively selecting a limited amount of valuable samples for labeling from the oracle. To solve this problem we devise an adaptive sampling strategy which jointly considers novelty informativeness and diversity to adaptively select novel samples with proper uncertainty. However owing to the varied orderings of label indices caused by the clustering of novel classes the queried labels are not directly applicable to subsequent training.
To solve this problem we devise an adaptive sampling strategy which jointly considers novelty informativeness and diversity to adaptively select novel samples with proper uncertainty. However owing to the varied orderings of label indices caused by the clustering of novel classes the queried labels are not directly applicable to subsequent training. To overcome this issue we further propose a stable label mapping algorithm that transforms ground truth labels to the label space of the classifier thereby ensuring consistent training across different active selection stages. Our method achieves state-of-the-art performance on both generic and fine-grained datasets. Our code is available at https://github.com/mashijie1028/ActiveGCD
Incorporating human feedback has been shown to be crucial to align text generated by large language models to human preferences. We hypothesize that state-of-the-art instructional image editing models where outputs are generated based on an input image and an editing instruction could similarly benefit from human feedback as their outputs may not adhere to the correct instructions and preferences of users. In this paper we present a novel framework to harness human feedback for instructional visual editing (HIVE). Specifically we collect human feedback on the edited images and learn a reward function to capture the underlying user preferences. We then introduce scalable diffusion model fine-tuning methods that can incorporate human preferences based on the estimated reward. Besides to mitigate the bias brought by the limitation of data we contribute a new 1.1M training dataset a 3.6K reward dataset for rewards learning and a 1K evaluation dataset to boost the performance of instructional image editing. We conduct extensive empirical experiments quantitatively and qualitatively showing that HIVE is favored over previous state-of-the-art instructional image editing approaches by a large margin.
Current 3D-aware facial NeRF generation approaches control the facial appearance by text lighting conditions or reference images limiting precise manipulation of local facial regions and interactivity. Color stroke a user-friendly and effective tool to depict appearance is challenging to edit 3D faces because of the lack of texture coarse geometry representation and detailed editing operations. To solve the above problems we introduce StrokeFaceNeRF a novel stroke-based method for editing facial NeRF appearance. In order to infer the missing texture and 3D geometry information 2D edited stroke maps are firstly encoded into the EG3D's latent space followed by a transformer-based editing module to achieve effective appearance changes while preserving the original geometry in editing regions. Notably we design a novel geometry loss function to ensure surface density remains consistent during training. To further enhance the local manipulation accuracy we propose a stereo fusion approach which lifts the 2D mask (inferred from strokes or drawn by users) into 3D mask volume allowing explicit blending of the original and edited faces. Extensive experiments validate that the proposed method outperforms existing 2D and 3D methods in both editing reality and geometry retention.
Generating emotional talking faces is a practical yet challenging endeavor. To create a lifelike avatar we draw upon two critical insights from a human perspective: 1) The connection between audio and the non-deterministic facial dynamics encompassing expressions blinks poses should exhibit synchronous and one-to-many mapping. 2) Vibrant expressions are often accompanied by emotion-aware high-definition (HD) textures and finely detailed teeth. However both aspects are frequently overlooked by existing methods. To this end this paper proposes using normalizing Flow and Vector-Quantization modeling to produce emotional talking faces that satisfy both insights concurrently (FlowVQTalker). Specifically we develop a flowbased coefficient generator that encodes the dynamics of facial emotion into a multi-emotion-class latent space represented as a mixture distribution. The generation process commences with random sampling from the modeled distribution guided by the accompanying audio enabling both lip-synchronization and the uncertain nonverbal facial cues generation. Furthermore our designed vector-quantization image generator treats the creation of expressive facial images as a code query task utilizing a learned codebook to provide rich high-quality textures that enhance the emotional perception of the results. Extensive experiments are conducted to showcase the effectiveness of our approach.
Most existing attention prediction research focuses on salient instances like humans and objects. However the more complex interaction-oriented attention arising from the comprehension of interactions between instances by human observers remains largely unexplored. This is equally crucial for advancing human-machine interaction and human-centered artificial intelligence. To bridge this gap we first collect a novel gaze fixation dataset named IG comprising 530000 fixation points across 740 diverse interaction categories capturing visual attention during human observers' cognitive processes of interactions. Subsequently we introduce the zero-shot interaction-oriented attention prediction task (ZeroIA) which challenges models to predict visual cues for interactions not encountered during training. Thirdly we present the Interactive Attention model (IA) designed to emulate human observers' cognitive processes to tackle the ZeroIA problem. Extensive experiments demonstrate that the proposed IA outperforms other state-of-the-art approaches in both ZeroIA and fully supervised settings. Lastly we endeavor to apply interaction-oriented attention to the interaction recognition task itself. Further experimental results demonstrate the promising potential to enhance the performance and interpretability of existing state-of-the-art HOI models by incorporating real human attention data from IG and attention labels generated by IA.
Learning-based approaches to monocular motion capture have recently shown promising results by learning to regress in a data-driven manner. However due to the challenges in data collection and network designs it remains challenging to achieve real-time full-body capture while being accurate in world space. In this work we introduce ProxyCap a human-centric proxy-to-motion learning scheme to learn world-space motions from a proxy dataset of 2D skeleton sequences and 3D rotational motions. Such proxy data enables us to build a learning-based network with accurate world-space supervision while also mitigating the generalization issues. For more accurate and physically plausible predictions in world space our network is designed to learn human motions from a human-centric perspective which enables the understanding of the same motion captured with different camera trajectories. Moreover a contact-aware neural motion descent module is proposed to improve foot-ground contact and motion misalignment with the proxy observations. With the proposed learning-based solution we demonstrate the first real-time monocular full-body capture system with plausible foot-ground contact in world space even using hand-held cameras.
Text-to-image generative models are becoming increasingly popular and accessible to the general public. As these models see large-scale deployments it is necessary to deeply investigate their safety and fairness to not disseminate and perpetuate any kind of biases. However existing works focus on detecting closed sets of biases defined a priori limiting the studies to well-known concepts. In this paper we tackle the challenge of open-set bias detection in text-to-image generative models presenting OpenBias a new pipeline that identifies and quantifies the severity of biases agnostically without access to any precompiled set. OpenBias has three stages. In the first phase we leverage a Large Language Model (LLM) to propose biases given a set of captions. Secondly the target generative model produces images using the same set of captions. Lastly a Vision Question Answering model recognizes the presence and extent of the previously proposed biases. We study the behavior of Stable Diffusion 1.5 2 and XL emphasizing new biases never investigated before. Via quantitative experiments we demonstrate that OpenBias agrees with current closed-set bias detection methods and human judgement.
Recent advances in monocular depth estimation have been made by incorporating natural language as additional guidance. Although yielding impressive results the impact of the language prior particularly in terms of generalization and robustness remains unexplored. In this paper we address this gap by quantifying the impact of this prior and introduce methods to benchmark its effectiveness across various settings. We generate "low-level" sentences that convey object-centric three-dimensional spatial relationships incorporate them as additional language priors and evaluate their downstream impact on depth estimation. Our key finding is that current language-guided depth estimators perform optimally only with scene-level descriptions and counter-intuitively fare worse with low level descriptions. Despite leveraging additional data these methods are not robust to directed adversarial attacks and decline in performance with an increase in distribution shift. Finally to provide a foundation for future research we identify points of failures and offer insights to better understand these shortcomings. With an increasing number of methods using language for depth estimation our findings highlight the opportunities and pitfalls that require careful consideration for effective deployment in real-world settings.
Text-to-image diffusion models have demonstrated remarkable capabilities in transforming text prompts into coherent images yet the computational cost of the multi-step inference remains a persistent challenge. To address this issue we present UFOGen a novel generative model designed for ultra-fast one-step text-to-image generation. In contrast to conventional approaches that focus on improving samplers or employing distillation techniques for diffusion models UFOGen adopts a hybrid methodology integrating diffusion models with a GAN objective. Leveraging a newly introduced diffusion-GAN objective and initialization with pre-trained diffusion models UFOGen excels in efficiently generating high-quality images conditioned on textual descriptions in a single step. Beyond traditional text-to-image generation UFOGen showcases versatility in applications. Notably UFOGen stands among the pioneering models enabling one-step text-to-image generation and diverse downstream tasks presenting a significant advancement in the landscape of efficient generative models.
3DiffTection introduces a novel method for 3D object detection from single images utilizing a 3D-aware diffusion model for feature extraction. Addressing the resource-intensive nature of annotating large-scale 3D image data our approach leverages pretrained diffusion models traditionally used for 2D tasks and adapts them for 3D detection through geometric and semantic tuning. Geometrically we enhance the model to perform view synthesis from single images incorporating an epipolar warp operator. This process utilizes easily accessible posed image data eliminating the need for manual annotation. Semantically the model is further refined on target detection data. Both stages utilize ControlNet ensuring the preservation of original feature capabilities. Through our methodology we obtain 3D-aware features that excel in identifying cross-view point correspondences. In 3D detection 3DiffTection substantially surpasses previous benchmarks e.g. Cube-RCNN by 9.43% in AP3D on the Omni3D-ARkitscene dataset. Furthermore 3DiffTection demonstrates robust label efficiency and generalizes well to cross-domain data nearly matching fully-supervised models in zero-shot scenarios.
In recent years there has been an explosion of 2D vision models for numerous tasks such as semantic segmentation style transfer or scene editing enabled by large-scale 2D image datasets. At the same time there has been renewed interest in 3D scene representations such as neural radiance fields from multi-view images. However the availability of 3D or multiview data is still substantially limited compared to 2D image datasets making extending 2D vision models to 3D data highly desirable but also very challenging. Indeed extending a single 2D vision operator like scene editing to 3D typically requires a highly creative method specialized to that task and often requires per-scene optimization. In this paper we ask the question of whether any 2D vision model can be lifted to make 3D consistent predictions. We answer this question in the affirmative; our new Lift3D method trains to predict unseen views on feature spaces generated by a few visual models (i.e. DINO and CLIP) but then generalizes to novel vision operators and tasks such as style transfer super-resolution open vocabulary segmentation and image colorization; for some of these tasks there is no comparable previous 3D method.
DINO and CLIP) but then generalizes to novel vision operators and tasks such as style transfer super-resolution open vocabulary segmentation and image colorization; for some of these tasks there is no comparable previous 3D method. In many cases we even outperform state-of-the-art methods specialized for the task in question. Moreover Lift3D is a zero-shot method in the sense that it requires no task-specific training nor scene-specific optimization.
In this paper we present a tensor decomposition and low-rank recovery approach (LowRankOcc) for vision-based 3D semantic occupancy prediction. Conventional methods model outdoor scenes with fine-grained 3D grids but the sparsity of non-empty voxels introduces considerable spatial redundancy leading to potential overfitting risks. In contrast our approach leverages the intrinsic low-rank property of 3D occupancy data factorizing voxel representations into low-rank components to efficiently mitigate spatial redundancy without sacrificing performance. Specifically we present the Vertical-Horizontal (VH) decomposition block factorizes 3D tensors into vertical vectors and horizontal matrices. With our "decomposition-encoding-recovery" framework we encode 3D contexts with only 1/2D convolutions and poolings and subsequently recover the encoded compact yet informative context features back to voxel representations. Experimental results demonstrate that LowRankOcc achieves state-of-the-art performances in semantic scene completion on the SemanticKITTI dataset and 3D occupancy prediction on the nuScenes dataset.
We introduce a novel framework for multiway point cloud mosaicking (named Wednesday) designed to co-align sets of partially overlapping point clouds -- typically obtained from 3D scanners or moving RGB-D cameras -- into a unified coordinate system. At the core of our approach is ODIN a learned pairwise registration algorithm that iteratively identifies overlaps and refines attention scores employing a diffusion-based process for denoising pairwise correlation matrices to enhance matching accuracy. Further steps include constructing a pose graph from all point clouds performing rotation averaging a novel robust algorithm for re-estimating translations optimally in terms of consensus maximization and translation optimization. Finally the point cloud rotations and positions are optimized jointly by a diffusion-based approach. Tested on four diverse large-scale datasets our method achieves state-of-the-art pairwise and multiway registration results by a large margin on all benchmarks. Our code and models are available at https://github.com/jinsz/Multiway-Point-Cloud-Mosaicking-with-Diffusion-and-Global-Optimization.
In this paper we address single image-based novel view synthesis (NVS) by firstly integrating view-dependent effects (VDE) into the process. Our approach leverages camera motion priors to model VDE treating negative disparity as the representation of these effects in the scene. By identifying that specularities align with camera motion we infuse VDEs into input images by aggregating pixel colors along the negative depth region of epipolar lines. Additionally we introduce a relaxed volumetric rendering approximation enhancing efficiency by computing densities in a single pass for NVS from single images. Notably our method learns single-image NVS from image sequences alone making it a fully self-supervised learning approach that requires no depth or camera pose annotations. We present extensive experimental results and show that our proposed method can learn NVS with VDEs outperforming the SOTA single-view NVS methods on the RealEstate10k and MannequinChallenge datasets. Visit our project site https://kaist-viclab.github.io/monovde-site.