CVPR 2024 Papers

Denoising diffusion probabilistic models (DDPMs) employ a sequence of white Gaussian noise samples to generate an image. In analogy with GANs those noise maps could be considered as the latent code associated with the generated image. However this native noise space does not possess a convenient structure and is thus challenging to work with in editing tasks. Here we propose an alternative latent noise space for DDPM that enables a wide range of editing operations via simple means and present an inversion method for extracting these edit-friendly noise maps for any given image (real or synthetically generated). As opposed to the native DDPM noise space the edit-friendly noise maps do not have a standard normal distribution and are not statistically independent across timesteps. However they allow perfect reconstruction of any desired image and simple transformations on them translate into meaningful manipulations of the output image (e.g. shifting color edits). Moreover in text-conditional models fixing those noise maps while changing the text prompt modifies semantics while retaining structure. We illustrate how this property enables text-based editing of real images via the diverse DDPM sampling scheme (in contrast to the popular non-diverse DDIM inversion). We also show how it can be used within existing diffusion-based editing methods to improve their quality and diversity. The code of the method is attached to this submission.

Expressive human pose and shape estimation (a.k.a. 3D whole-body mesh recovery) involves the human body hand and expression estimation. Most existing methods have tackled this task in a two-stage manner first detecting the human body part with an off-the-shelf detection model and then inferring the different human body parts individually. Despite the impressive results achieved these methods suffer from 1) loss of valuable contextual information via cropping 2) introducing distractions and 3) lacking inter-association among different persons and body parts inevitably causing performance degradation especially for crowded scenes. To address these issues we introduce a novel all-in-one-stage framework AiOS for multiple expressive human pose and shape recovery without an additional human detection step. Specifically our method is built upon DETR which treats multi-person whole-body mesh recovery task as a progressive set prediction problem with various sequential detection. We devise the decoder tokens and extend them to our task. Specifically we first employ a human token to probe a human location in the image and encode global features for each instance which provides a coarse location for the later transformer block. Then we introduce a joint-related token to probe the human joint in the image and encoder a fine-grained local feature which collaborates with the global feature to regress the whole-body mesh. This straightforward but effective model outperforms previous state-of-the-art methods by a 9 reduction in NMVE on AGORA a 30 reduction in PVE on EHF a 10 reduction in PVE on ARCTIC and a 3 reduction in PVE on EgoBody.

In this paper we address web-scale visual entity recognition specifically the task of mapping a given query image to one of the 6 million existing entities in Wikipedia. One way of approaching a problem of such scale is using dual encoder models (e.g. CLIP) where all the entity names and query images are embedded into a unified space paving the way for an approximate kNN search. Alternatively it is also possible to re-purpose a captioning model to directly generate the entity names for a given image. In contrast we introduce a novel Generative Entity Recognition (GER) framework which given an input image learns to auto-regressively decode a semantic and discriminative "code" identifying the target entity. Our experiments demonstrate the efficacy of this GER paradigm showcasing state-of-the-art performance on the challenging OVEN benchmark. GER surpasses strong captioning dual-encoder visual matching and hierarchical classification baselines affirming its advantage in tackling the complexities of web-scale recognition.

We present 3D Paintbrush a technique for automatically texturing local semantic regions on meshes via text descriptions. Our method is designed to operate directly on meshes producing texture maps which seamlessly integrate into standard graphics pipelines. We opt to simultaneously produce a localization map (to specify the edit region) and a texture map which conforms to it. This approach improves the quality of both the localization and the stylization. To enhance the details and resolution of the textured area we leverage multiple stages of a cascaded diffusion model to supervise our local editing technique with generative priors learned from images at different resolutions. Our technique referred to as Cascaded Score Distillation (CSD) simultaneously distills scores at multiple resolutions in a cascaded fashion enabling control over both the granularity and global understanding of the supervision. We demonstrate the effectiveness of 3D Paintbrush to locally texture different semantic regions on a variety of shapes.