CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
The resulting stylized face model can be animated by commonly used 3DMM blend shapes. A set of quantitative and qualitative evaluations demonstrate that our method can produce highly stylized face meshes according to a given style and output them in a desired topology. We also demonstrate example applications of our method including image-based stylized avatar generation linear interpolation of geometric styles and facial animation of stylized avatars.
Dilated convolution which expands the receptive field by inserting gaps between its consecutive elements is widely employed in computer vision. In this study we propose three strategies to improve individual phases of dilated convolution from the view of spectrum analysis. Departing from the conventional practice of fixing a global dilation rate as a hyperparameter we introduce Frequency-Adaptive Dilated Convolution (FADC) which dynamically adjusts dilation rates spatially based on local frequency components. Subsequently we design two plug-in modules to directly enhance effective bandwidth and receptive field size. The Adaptive Kernel (AdaKern) module decomposes convolution weights into low-frequency and high-frequency components dynamically adjusting the ratio between these components on a per-channel basis. By increasing the high-frequency part of convolution weights AdaKern captures more high-frequency components thereby improving effective bandwidth. The Frequency Selection (FreqSelect) module optimally balances high- and low-frequency components in feature representations through spatially variant reweighting. It suppresses high frequencies in the background to encourage FADC to learn a larger dilation thereby increasing the receptive field for an expanded scope. Extensive experiments on segmentation and object detection consistently validate the efficacy of our approach. The code is made publicly available at https://github.com/Linwei-Chen/FADC.
3D building reconstruction from monocular remote sensing images is an important and challenging research problem that has received increasing attention in recent years owing to its low cost of data acquisition and availability for large-scale applications. However existing methods rely on expensive 3D-annotated samples for fully-supervised training restricting their application to large-scale cross-city scenarios. In this work we propose MLS-BRN a multi-level supervised building reconstruction network that can flexibly utilize training samples with different annotation levels to achieve better reconstruction results in an end-to-end manner. To alleviate the demand on full 3D supervision we design two new modules Pseudo Building Bbox Calculator and Roof-Offset guided Footprint Extractor as well as new tasks and training strategies for different types of samples. Experimental results on several public and new datasets demonstrate that our proposed MLS-BRN achieves competitive performance using much fewer 3D-annotated samples and significantly improves the footprint extraction and 3D reconstruction performance compared with current state-of-the-art. The code and datasets of this work will be released at https://github.com/opendatalab/MLS-BRN.git.
With recent developments in Embodied Artificial Intelligence (EAI) research there has been a growing demand for high-quality large-scale interactive scene generation. While prior methods in scene synthesis have prioritized the naturalness and realism of the generated scenes the physical plausibility and interactivity of scenes have been largely left unexplored. To address this disparity we introduce PhyScene a novel method dedicated to generating interactive 3D scenes characterized by realistic layouts articulated objects and rich physical interactivity tailored for embodied agents. Based on a conditional diffusion model for capturing scene layouts we devise novel physics- and interactivity-based guidance mechanisms that integrate constraints from object collision room layout and object reachability. Through extensive experiments we demonstrate that PhyScene effectively leverages these guidance functions for physically interactable scene synthesis outperforming existing state-of-the-art scene synthesis methods by a large margin. Our findings suggest that the scenes generated by PhyScene hold considerable potential for facilitating diverse skill acquisition among agents within interactive environments thereby catalyzing further advancements in embodied AI research.
Most existing image compression approaches perform transform coding in the pixel space to reduce its spatial redundancy. However they encounter difficulties in achieving both high-realism and high-fidelity at low bitrate as the pixel-space distortion may not align with human perception. To address this issue we introduce a Generative Latent Coding (GLC) architecture which performs transform coding in the latent space of a generative vector-quantized variational auto-encoder (VQ-VAE) instead of in the pixel space. The generative latent space is characterized by greater sparsity richer semantic and better alignment with human perception rendering it advantageous for achieving high-realism and high-fidelity compression. Additionally we introduce a categorical hyper module to reduce the bit cost of hyper-information and a code-prediction-based supervision to enhance the semantic consistency. Experiments demonstrate that our GLC maintains high visual quality with less than 0.04 bpp on natural images and less than 0.01 bpp on facial images. On the CLIC2020 test set we achieve the same FID as MS-ILLM with 45% fewer bits. Furthermore the powerful generative latent space enables various applications built on our GLC pipeline such as image restoration and style transfer.
In this work we aim to improve the 3D reasoning ability of Transformers in multi-view 3D human pose estimation. Recent works have focused on end-to-end learning-based transformer designs which struggle to resolve geometric information accurately particularly during occlusion. Instead we propose a novel hybrid model MVGFormer which has a series of geometric and appearance modules organized in an iterative manner. The geometry modules are learning-free and handle all viewpoint-dependent 3D tasks geometrically which notably improves the model's generalization ability. The appearance modules are learnable and are dedicated to estimating 2D poses from image signals end-to-end which enables them to achieve accurate estimates even when occlusion occurs leading to a model that is both accurate and generalizable to new cameras and geometries. We evaluate our approach for both in-domain and out-of-domain settings where our model consistently outperforms state-of-the-art methods and especially does so by a significant margin in the out-of-domain setting. We will release the code and models: https://github.com/XunshanMan/MVGFormer.
A long-standing goal of 3D human reconstruction is to create lifelike and fully detailed 3D humans from single-view images. The main challenge lies in inferring unknown body shapes appearances and clothing details in areas not visible in the images. To address this we propose SiTH a novel pipeline that uniquely integrates an image-conditioned diffusion model into a 3D mesh reconstruction workflow. At the core of our method lies the decomposition of the challenging single-view reconstruction problem into generative hallucination and reconstruction subproblems. For the former we employ a powerful generative diffusion model to hallucinate unseen back-view appearance based on the input images. For the latter we leverage skinned body meshes as guidance to recover full-body texture meshes from the input and back-view images. SiTH requires as few as 500 3D human scans for training while maintaining its generality and robustness to diverse images. Extensive evaluations on two 3D human benchmarks including our newly created one highlighted our method's superior accuracy and perceptual quality in 3D textured human reconstruction. Our code and evaluation benchmark is available at https://ait.ethz.ch/sith.
Facial Attribute Classification (FAC) holds substantial promise in widespread applications. However FAC models trained by traditional methodologies can be unfair by exhibiting accuracy inconsistencies across varied data subpopulations. This unfairness is largely attributed to bias in data where some spurious attributes (e.g. Male) statistically correlate with the target attribute (e.g. Smiling). Most of existing fairness-aware methods rely on the labels of spurious attributes which may be unavailable in practice. This work proposes a novel generation-based two-stage framework to train a fair FAC model on biased data without additional annotation. Initially we identify the potential spurious attributes based on generative models. Notably it enhances interpretability by explicitly showing the spurious attributes in image space. Following this for each image we first edit the spurious attributes with a random degree sampled from a uniform distribution while keeping target attribute unchanged. Then we train a fair FAC model by fostering model invariance to these augmentation. Extensive experiments on three common datasets demonstrate the effectiveness of our method in promoting fairness in FAC without compromising accuracy. Codes are in https://github.com/heqianpei/DiGA.
Despite recent progress in diffusion-based video editing existing methods are limited to short-length videos due to the contradiction between long-range consistency and frame-wise editing. Prior attempts to address this challenge by introducing video-2D representations encounter significant difficulties with large motion- and view-change videos especially in human-centric scenarios. To overcome this we propose to introduce the dynamic Neural Radiance Fields (NeRF) as the innovative video representation where the editing can be performed in the 3D spaces and propagated to the entire video via the deformation field. To provide consistent and controllable editing we propose the image-based video-NeRF editing pipeline with a set of innovative designs including multi-view multi-pose Score Distillation Sampling (SDS) from both the 2D personalized diffusion prior and 3D diffusion prior reconstruction losses text-guided local parts super-resolution and style transfer. Extensive experiments demonstrate that our method dubbed as DynVideo-E significantly outperforms SOTA approaches on two challenging datasets by a large margin of 50%   95% for human preference. Code will be released at https://showlab.github.io/DynVideo-E/.
We propose a neural reflectance model (NeuBRDF) that offers highly versatile material representation yet with light memory and neural computation consumption towards achieving real-time rendering. The results depicted in Fig. 1 rendered at full HD resolution on a contemporary desktop machine demonstrate that our system achieves real-time performance with a wide variety of appearances which is approached by the following two designs. Firstly recognizing that the bidirectional reflectance is distributed in a sparse high-dimensional space we propose to project the BRDF into two low-dimensional components i.e. two hemisphere feature-grids for incoming and outgoing directions respectively. Secondly we distribute learnable neural reflectance primitives on our highly-tailored spherical surface grid. These primitives offer informative features for each hemisphere component and reduce the complexity of the feature learning network leading to fast evaluation. These primitives are centrally stored in a codebook and can be shared across multiple grids and even across materials based on low-cost indices stored in material-specific spherical surface grids. Our NeuBRDF agnostic to the material provides a unified framework for representing a variety of materials consistently. Comprehensive experimental results on measured BRDF compression Monte Carlo simulated BRDF acceleration and extension to spatially varying effects demonstrate the superior quality and generalizability achieved by the proposed scheme.
Video stabilization is a longstanding computer vision problem particularly pixel-level synthesis solutions for video stabilization which synthesize full frames add to the complexity of this task. These techniques aim to stabilize videos by synthesizing full frames while enhancing the stability of the considered video. This intensifies the complexity of the task due to the distinct mix of unique motion profiles and visual content present in each video sequence making robust generalization with fixed parameters difficult. In our study we introduce a novel approach to enhance the performance of pixel-level synthesis solutions for video stabilization by adapting these models to individual input video sequences. The proposed adaptation exploits low-level visual cues accessible during test-time to improve both the stability and quality of resulting videos. We highlight the efficacy of our methodology of "test-time adaptation" through simple fine-tuning of one of these models followed by significant stability gain via the integration of meta-learning techniques. Notably significant improvement is achieved with only a single adaptation step. The versatility of the proposed algorithm is demonstrated by consistently improving the performance of various pixel-level synthesis models for video stabilization in real-world scenarios.
Text-to-video generation aims to produce a video based on a given prompt. Recently several commercial video models have been able to generate plausible videos with minimal noise excellent details and high aesthetic scores. However these models rely on large-scale well-filtered high-quality videos that are not accessible to the community. Many existing research works which train models using the low-quality WebVid-10M dataset struggle to generate high-quality videos because the models are optimized to fit WebVid-10M. In this work we explore the training scheme of video models extended from Stable Diffusion and investigate the feasibility of leveraging low-quality videos and synthesized high-quality images to obtain a high-quality video model. We first analyze the connection between the spatial and temporal modules of video models and the distribution shift to low-quality videos. We observe that full training of all modules results in a stronger coupling between spatial and temporal modules than only training temporal modules. Based on this stronger coupling we shift the distribution to higher quality without motion degradation by finetuning spatial modules with high-quality images resulting in a generic high-quality video model. Evaluations are conducted to demonstrate the superiority of the proposed method particularly in picture quality motion and concept composition.
Weakly Supervised Semantic Segmentation (WSSS) aims to learn the concept of segmentation using image-level class labels. Recent WSSS works have shown promising results by using the Segment Anything Model (SAM) a foundation model for segmentation during the inference phase. However we observe that these methods can still be vulnerable to the noise of class activation maps (CAMs) serving as initial seeds. As a remedy this paper introduces From-SAM-to-CAMs (S2C) a novel WSSS framework that directly transfers the knowledge of SAM to the classifier during the training process enhancing the quality of CAMs itself. S2C comprises SAM-segment Contrasting (SSC) and a CAM-based prompting module (CPM) which exploit SAM at the feature and logit levels respectively. SSC performs prototype-based contrasting using SAM's automatic segmentation results. It constrains each feature to be close to the prototype of its segment and distant from prototypes of the others. Meanwhile CPM extracts prompts from the CAM of each class and uses them to generate class-specific segmentation masks through SAM. The masks are aggregated into unified self-supervision based on the confidence score designed to consider the reliability of both SAM and CAMs.
Meanwhile CPM extracts prompts from the CAM of each class and uses them to generate class-specific segmentation masks through SAM. The masks are aggregated into unified self-supervision based on the confidence score designed to consider the reliability of both SAM and CAMs. S2C achieves a new state-of-the-art performance across all benchmarks outperforming existing studies by significant margins. The code is available at https://github.com/sangrockEG/S2C.
Flow-based super-resolution (SR) models have demonstrated astonishing capabilities in generating high-quality images. However these methods encounter several challenges during image generation such as grid artifacts exploding inverses and suboptimal results due to a fixed sampling temperature. To overcome these issues this work introduces a conditional learned prior to the inference phase of a flow-based SR model. This prior is a latent code predicted by our proposed latent module conditioned on the low-resolution image which is then transformed by the flow model into an SR image. Our framework is designed to seamlessly integrate with any contemporary flow-based SR model without modifying its architecture or pre-trained weights. We evaluate the effectiveness of our proposed framework through extensive experiments and ablation analyses. The proposed framework successfully addresses all the inherent issues in flow-based SR models and enhances their performance in various SR scenarios. Our code is available at: https://github.com/liyuantsao/FlowSR-LP
In this paper we propose a novel abstraction-aware sketch-based image retrieval framework capable of handling sketch abstraction at varied levels. Prior works had mainly focused on tackling sub-factors such as drawing style and order we instead attempt to model abstraction as a whole and propose feature-level and retrieval granularity-level designs so that the system builds into its DNA the necessary means to interpret abstraction. On learning abstraction-aware features we for the first-time harness the rich semantic embedding of pre-trained StyleGAN model together with a novel abstraction-level mapper that deciphers the level of abstraction and dynamically selects appropriate dimensions in the feature matrix correspondingly to construct a feature matrix embedding that can be freely traversed to accommodate different levels of abstraction. For granularity-level abstraction understanding we dictate that the retrieval model should not treat all abstraction-levels equally and introduce a differentiable surrogate Acc.@q loss to inject that understanding into the system. Different to the gold-standard triplet loss our Acc.@q loss uniquely allows a sketch to narrow/broaden its focus in terms of how stringent the evaluation should be - the more abstract a sketch the less stringent (higher q). Extensive experiments depict our method to outperform existing state-of-the-arts in standard SBIR tasks along with challenging scenarios like early retrieval forensic sketch-photo matching and style-invariant retrieval.
3D-aware Generative Adversarial Networks (GANs) have shown remarkable progress in learning to generate multi-view-consistent images and 3D geometries of scenes from collections of 2D images via neural volume rendering. Yet the significant memory and computational costs of dense sampling in volume rendering have forced 3D GANs to adopt patch-based training or employ low-resolution rendering with post-processing 2D super resolution which sacrifices multiview consistency and the quality of resolved geometry. Consequently 3D GANs have not yet been able to fully resolve the rich 3D geometry present in 2D images. In this work we propose techniques to scale neural volume rendering to the much higher resolution of native 2D images thereby resolving fine-grained 3D geometry with unprecedented detail. Our approach employs learning-based samplers for accelerating neural rendering for 3D GAN training using up to 5 times fewer depth samples. This enables us to explicitly "render every pixel" of the full-resolution image during training and inference without post-processing superresolution in 2D.
Our approach employs learning-based samplers for accelerating neural rendering for 3D GAN training using up to 5 times fewer depth samples. This enables us to explicitly "render every pixel" of the full-resolution image during training and inference without post-processing superresolution in 2D. Together with our strategy to learn high-quality surface geometry our method synthesizes high-resolution 3D geometry and strictly view-consistent images while maintaining image quality on par with baselines relying on post-processing super resolution. We demonstrate state-of-the-art 3D gemetric quality on FFHQ and AFHQ setting a new standard for unsupervised learning of 3D shapes in 3D GANs.
Despite the impressive generative capabilities of diffusion models existing diffusion model-based style transfer methods require inference-stage optimization (e.g. fine-tuning or textual inversion of style) which is time-consuming or fails to leverage the generative ability of large-scale diffusion models. To address these issues we introduce a novel artistic style transfer method based on a pre-trained large-scale diffusion model without any optimization. Specifically we manipulate the features of self-attention layers as the way the cross-attention mechanism works; in the generation process substituting the key and value of content with those of style image. This approach provides several desirable characteristics for style transfer including 1) preservation of content by transferring similar styles into similar image patches and 2) transfer of style based on similarity of local texture (e.g. edge) between content and style images. Furthermore we introduce query preservation and attention temperature scaling to mitigate the issue of disruption of original content and initial latent Adaptive Instance Normalization (AdaIN) to deal with the disharmonious color (failure to transfer the colors of style). Our experimental results demonstrate that our proposed method surpasses state-of-the-art methods in both conventional and diffusion-based style transfer baselines.
Learning to optimize (L2O) is an emerging technique to solve mathematical optimization problems with learning-based methods. Although with great success in many real-world scenarios such as wireless communications computer networks and electronic design existing L2O works lack theoretical demonstration of their performance and robustness in out-of-distribution (OOD) scenarios. We address this gap by providing comprehensive proofs. First we prove a sufficient condition for a robust L2O model with homogeneous convergence rates over all In-Distribution (InD) instances. We assume an L2O model achieves robustness for an InD scenario. Based on our proposed methodology of aligning OOD problems to InD problems we also demonstrate that the L2O model's convergence rate in OOD scenarios will deteriorate by an equation of the L2O model's input features. Moreover we propose an L2O model with a concise gradient-only feature construction and a novel gradient-based history modeling method. Numerical simulation demonstrates that our proposed model outperforms the state-of-the-art baseline in both InD and OOD scenarios and achieves up to 10 xconvergence speedup. The code of our method can be found from https://github.com/NetX-lab/GoMathL2O-Official.