CVPR 2024 Papers

We introduce LightIt a method for explicit illumination control for image generation. Recent generative methods lack lighting control which is crucial to numerous artistic aspects of image generation such as setting the overall mood or cinematic appearance. To overcome these limitations we propose to condition the generation on shading and normal maps. We model the lighting with single bounce shading which includes cast shadows. We first train a shading estimation module to generate a dataset of real-world images and shading pairs. Then we train a control network using the estimated shading and normals as input. Our method demonstrates high-quality image generation and lighting control in numerous scenes. Additionally we use our generated dataset to train an identity-preserving relighting model conditioned on an image and a target shading. Our method is the first that enables the generation of images with controllable consistent lighting and performs on par with specialized relighting state-of-the-art methods.

3D visual language multi-modal modeling plays an important role in actual human-computer interaction. However the inaccessibility of large-scale 3D-language pairs restricts their applicability in real-world scenarios. In this paper we aim to handle a real-time multi-task for 6-DoF pose tracking of unknown objects leveraging 3D-language pre-training scheme from a series of 3D point cloud video streams while simultaneously performing 3D shape reconstruction in current observation. To this end we present a generic Language-to-4D modeling paradigm termed L4D-Track that tackles zero-shot 6-DoF \underline Track ing and shape reconstruction by learning pairwise implicit 3D representation and multi-level multi-modal alignment. Our method constitutes two core parts. 1) Pairwise Implicit 3D Space Representation that establishes spatial-temporal to language coherence descriptions across continuous 3D point cloud video. 2) Language-to-4D Association and Contrastive Alignment enables multi-modality semantic connections between 3D point cloud video and language. Our method trained exclusively on public NOCS-REAL275 dataset achieves promising results on both two publicly benchmarks. This not only shows powerful generalization performance but also proves its remarkable capability in zero-shot inference.

Infrared physical adversarial examples are of great significance for studying the security of infrared AI systems that are widely used in our lives such as autonomous driving. Previous infrared physical attacks mainly focused on 2D infrared pedestrian detection which may not fully manifest its destructiveness to AI systems. In this work we propose a physical attack method against infrared detectors based on 3D modeling which is applied to a real car. The goal is to design a set of infrared adversarial stickers to make cars invisible to infrared detectors at various viewing angles distances and scenes. We build a 3D infrared car model with real infrared characteristics and propose an infrared adversarial pattern generation method based on 3D mesh shadow. We propose a 3D control points-based mesh smoothing algorithm and use a set of smoothness loss functions to enhance the smoothness of adversarial meshes and facilitate the sticker implementation. Besides We designed the aluminum stickers and conducted physical experiments on two real Mercedes-Benz A200L cars. Our adversarial stickers hid the cars from Faster RCNN an object detector at various viewing angles distances and scenes. The attack success rate (ASR) was 91.49% for real cars. In comparison the ASRs of random stickers and no sticker were only 6.21% and 0.66% respectively. In addition the ASRs of the designed stickers against six unseen object detectors such as YOLOv3 and Deformable DETR were between 73.35%-95.80% showing good transferability of the attack performance across detectors.

Inspired by the long-range modeling ability of ViTs large-kernel convolutions are widely studied and adopted recently to enlarge the receptive field and improve model performance like the remarkable work ConvNeXt which employs 7x7 depthwise convolution. Although such depthwise operator only consumes a few FLOPs it largely harms the model efficiency on powerful computing devices due to the high memory access costs. For example ConvNeXt-T has similar FLOPs with ResNet-50 but only achieves 60% throughputs when trained on A100 GPUs with full precision. Although reducing the kernel size of ConvNeXt can improve speed it results in significant performance degradation which poses a challenging problem: How to speed up large-kernel-based CNN models while preserving their performance. To tackle this issue inspired by Inceptions we propose to decompose large-kernel depthwise convolution into four parallel branches along channel dimension i.e. small square kernel two orthogonal band kernels and an identity mapping. With this new Inception depthwise convolution we build a series of networks namely IncepitonNeXt which not only enjoy high throughputs but also maintain competitive performance. For instance InceptionNeXt-T achieves 1.6x higher training throughputs than ConvNeX-T as well as attains 0.2% top-1 accuracy improvement on ImageNet-1K. We anticipate InceptionNeXt can serve as an economical baseline for future architecture design to reduce carbon footprint.

We introduce GoMAvatar a novel approach for real-time memory-efficient high-quality animatable human modeling. GoMAvatar takes as input a single monocular video to create a digital avatar capable of re-articulation in new poses and real-time rendering from novel viewpoints while seamlessly integrating with rasterization-based graphics pipelines. Central to our method is the Gaussians-on-Mesh (GoM) representation a hybrid 3D model combining rendering quality and speed of Gaussian splatting with geometry modeling and compatibility of deformable meshes. We assess GoMAvatar on ZJU-MoCap PeopleSnapshot and various YouTube videos. GoMAvatar matches or surpasses current monocular human modeling algorithms in rendering quality and significantly outperforms them in computational efficiency (43 FPS) while being memory-efficient (3.63 MB per subject).

We present GaussianAvatar an efficient approach to creating realistic human avatars with dynamic 3D appearances from a single video. We start by introducing animatable 3D Gaussians to explicitly represent humans in various poses and clothing styles. Such an explicit and animatable representation can fuse 3D appearances more efficiently and consistently from 2D observations. Our representation is further augmented with dynamic properties to support pose-dependent appearance modeling where a dynamic appearance network along with an optimizable feature tensor is designed to learn the motion-to-appearance mapping. Moreover by leveraging the differentiable motion condition our method enables a joint optimization of motions and appearances during avatar modeling which helps to tackle the long-standing issue of inaccurate motion estimation in monocular settings. The efficacy of GaussianAvatar is validated on both the public dataset and our collected dataset demonstrating its superior performances in terms of appearance quality and rendering efficiency.

Creating high-fidelity 3D head avatars has always been a research hotspot but there remains a great challenge under lightweight sparse view setups. In this paper we propose Gaussian Head Avatar represented by controllable 3D Gaussians for high-fidelity head avatar modeling. We optimize the neutral 3D Gaussians and a fully learned MLP-based deformation field to capture complex expressions. The two parts benefit each other thereby our method can model fine-grained dynamic details while ensuring expression accuracy. Furthermore we devise a well-designed geometry-guided initialization strategy based on implicit SDF and Deep Marching Tetrahedra for the stability and convergence of the training procedure. Experiments show our approach outperforms other state-of-the-art sparse-view methods achieving ultra high-fidelity rendering quality at 2K resolution even under exaggerated expressions. Project page: https://yuelangx.github.io/gaussianheadavatar.

Event-based Vision Sensors (EVS) gain popularity in enhancing CMOS Image Sensor (CIS) video capture. Nonidealities of EVS such as pixel or readout latency can significantly influence the quality of the enhanced images and warrant dedicated consideration in the design of fusion algorithms. A novel approach for jointly computing deblurred rolling-shutter artifact corrected high-speed videos with frame rates up to 10000 FPS using inherently blurry rolling shutter CIS frames of 120 FPS to 150 FPS in conjunction with EVS data from a hybrid CIS-EVS sensor is presented. EVS pixel latency readout latency and the sensor's refractory period are explicitly incorporated into the measurement model. This inverse function problem is solved on a per-pixel manner using an optimization-based framework. The interpolated images are subsequently processed by a novel refinement network. The proposed method is evaluated using simulated and measured datasets under natural and controlled environments. Extensive experiments show reduced shadowing effect a 4 dB increment in PSNR and a 12% improvement in LPIPS score compared to state-of-the-art methods.

Neural implicit surface reconstruction leveraging volume rendering has led to significant advances in multi-view reconstruction. However results for transparent objects can be very poor primarily because the rendering function fails to account for the intricate light transport induced by refraction and reflection. In this study we introduce transparent neural surface refinement (TNSR) a novel surface reconstruction framework that explicitly incorporates physical refraction and reflection tracing. Beginning with an initial approximate surface our method employs sphere tracing combined with Snell's law to cast both reflected and refracted rays. Central to our proposal is an innovative differentiable technique devised to allow signals from the photometric evidence to propagate back to the surface model by considering how the surface bends and reflects light rays. This allows us to connect surface refinement with volume rendering enabling end-to-end optimization solely on multi-view RGB images. In our experiments TNSR demonstrates significant improvements in novel view synthesis and geometry estimation of transparent objects without prior knowledge of the refractive index.

Modeling animatable human avatars from RGB videos is a long-standing and challenging problem. Recent works usually adopt MLP-based neural radiance fields (NeRF) to represent 3D humans but it remains difficult for pure MLPs to regress pose-dependent garment details. To this end we introduce Animatable Gaussians a new avatar representation that leverages powerful 2D CNNs and 3D Gaussian splatting to create high-fidelity avatars. To associate 3D Gaussians with the animatable avatar we learn a parametric template from the input videos and then parameterize the template on two front & back canonical Gaussian maps where each pixel represents a 3D Gaussian. The learned template is adaptive to the wearing garments for modeling looser clothes like dresses. Such template-guided 2D parameterization enables us to employ a powerful StyleGAN-based CNN to learn the pose-dependent Gaussian maps for modeling detailed dynamic appearances. Furthermore we introduce a pose projection strategy for better generalization given novel poses. Overall our method can create lifelike avatars with dynamic realistic and generalized appearances. Experiments show that our method outperforms other state-of-the-art approaches. Code: https://github.com/lizhe00/AnimatableGaussians.