Diffusing in Someone Else's Shoes: Robotic Perspective Taking with Diffusion

Humanoid robots can benefit from their similarity to the human shape by learning from humans. When humans teach other humans how to perform actions, they often demonstrate the actions and the learning human can try to imitate the demonstration. Being able to mentally transfer from a demonstration seen from a third-person perspective to how it should look from a first-person perspective is fundamental for this ability in humans. As this is a challenging task, it is often simplified for robots by creating a demonstration in the first-person perspective. Creating these demonstrations requires more effort but allows for an easier imitation. We introduce a novel diffusion model aimed at enabling the robot to directly learn from the third-person demonstrations. Our model is capable of learning and generating the first-person perspective from the third-person perspective by translating the size and rotations of objects and the environment between two perspectives. This allows us to utilise the benefits of easy-to-produce third-person demonstrations and easy-to-imitate first-person demonstrations. The model can either represent the first-person perspective in an RGB image or calculate the joint values. Our approach significantly outperforms other image-to-image models in this task.

LiDAR-Guided Cross-Attention Fusion for Hyperspectral Band Selection and Image Classification

The fusion of hyperspectral and LiDAR data has been an active research topic. Existing fusion methods have ignored the high-dimensionality and redundancy challenges in hyperspectral images, despite that band selection methods have been intensively studied for hyperspectral image (HSI) processing. This paper addresses this significant gap by introducing a cross-attention mechanism from the transformer architecture for the selection of HSI bands guided by LiDAR data. LiDAR provides high-resolution vertical structural information, which can be useful in distinguishing different types of land cover that may have similar spectral signatures but different structural profiles. In our approach, the LiDAR data are used as the "query" to search and identify the "key" from the HSI to choose the most pertinent bands for LiDAR. This method ensures that the selected HSI bands drastically reduce redundancy and computational requirements while working optimally with the LiDAR data. Extensive experiments have been undertaken on three paired HSI and LiDAR data sets: Houston 2013, Trento and MUUFL. The results highlight the superiority of the cross-attention mechanism, underlining the enhanced classification accuracy of the identified HSI bands when fused with the LiDAR features. The results also show that the use of fewer bands combined with LiDAR surpasses the performance of state-of-the-art fusion models.

GAN with Skip Patch Discriminator for Biological Electron Microscopy Image Generation

Generating realistic electron microscopy (EM) images has been a challenging problem due to their complex global and local structures. Isola et al. proposed pix2pix, a conditional Generative Adversarial Network (GAN), for the general purpose of image-to-image translation; which fails to generate realistic EM images. We propose a new architecture for the discriminator in the GAN providing access to multiple patch sizes using skip patches and generating realistic EM images.

FlexiDreamer: Single Image-to-3D Generation with FlexiCubes

3D content generation from text prompts or single images has made remarkable progress in quality and speed recently. One of its dominant paradigms involves generating consistent multi-view images followed by a sparse-view reconstruction. However, due to the challenge of directly deforming the mesh representation to approach the target topology, most methodologies learn an implicit representation (such as NeRF) during the sparse-view reconstruction and acquire the target mesh by a post-processing extraction. Although the implicit representation can effectively model rich 3D information, its training typically entails a long convergence time. In addition, the post-extraction operation from the implicit field also leads to undesirable visual artifacts. In this paper, we propose FlexiDreamer, a novel single image-to-3d generation framework that reconstructs the target mesh in an end-to-end manner. By leveraging a flexible gradient-based extraction known as FlexiCubes, our method circumvents the defects brought by the post-processing and facilitates a direct acquisition of the target mesh. Furthermore, we incorporate a multi-resolution hash grid encoding scheme that progressively activates the encoding levels into the implicit field in FlexiCubes to help capture geometric details for per-step optimization. Notably, FlexiDreamer recovers a dense 3D structure from a single-view image in approximately 1 minute on a single NVIDIA A100 GPU, outperforming previous methodologies by a large margin.

Connecting NeRFs, Images, and Text

Neural Radiance Fields (NeRFs) have emerged as a standard framework for representing 3D scenes and objects, introducing a novel data type for information exchange and storage. Concurrently, significant progress has been made in multimodal representation learning for text and image data. This paper explores a novel research direction that aims to connect the NeRF modality with other modalities, similar to established methodologies for images and text. To this end, we propose a simple framework that exploits pre-trained models for NeRF representations alongside multimodal models for text and image processing. Our framework learns a bidirectional mapping between NeRF embeddings and those obtained from corresponding images and text. This mapping unlocks several novel and useful applications, including NeRF zero-shot classification and NeRF retrieval from images or text.

Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach

Event camera has recently received much attention for low-light image enhancement (LIE) thanks to their distinct advantages, such as high dynamic range. However, current research is prohibitively restricted by the lack of large-scale, real-world, and spatial-temporally aligned event-image datasets. To this end, we propose a real-world (indoor and outdoor) dataset comprising over 30K pairs of images and events under both low and normal illumination conditions. To achieve this, we utilize a robotic arm that traces a consistent non-linear trajectory to curate the dataset with spatial alignment precision under 0.03mm. We then introduce a matching alignment strategy, rendering 90% of our dataset with errors less than 0.01s. Based on the dataset, we propose a novel event-guided LIE approach, called EvLight, towards robust performance in real-world low-light scenes. Specifically, we first design the multi-scale holistic fusion branch to extract holistic structural and textural information from both events and images. To ensure robustness against variations in the regional illumination and noise, we then introduce a Signal-to-Noise-Ratio (SNR)-guided regional feature selection to selectively fuse features of images from regions with high SNR and enhance those with low SNR by extracting regional structure information from events. Extensive experiments on our dataset and the synthetic SDSD dataset demonstrate our EvLight significantly surpasses the frame-based methods. Code and datasets are available at https://vlislab22.github.io/eg-lowlight/.

Solving Masked Jigsaw Puzzles with Diffusion Vision Transformers

Solving image and video jigsaw puzzles poses the challenging task of rearranging image fragments or video frames from unordered sequences to restore meaningful images and video sequences. Existing approaches often hinge on discriminative models tasked with predicting either the absolute positions of puzzle elements or the permutation actions applied to the original data. Unfortunately, these methods face limitations in effectively solving puzzles with a large number of elements. In this paper, we propose JPDVT, an innovative approach that harnesses diffusion transformers to address this challenge. Specifically, we generate positional information for image patches or video frames, conditioned on their underlying visual content. This information is then employed to accurately assemble the puzzle pieces in their correct positions, even in scenarios involving missing pieces. Our method achieves state-of-the-art performance on several datasets.

Incremental Self-training for Semi-supervised Learning

Semi-supervised learning provides a solution to reduce the dependency of machine learning on labeled data. As one of the efficient semi-supervised techniques, self-training (ST) has received increasing attention. Several advancements have emerged to address challenges associated with noisy pseudo-labels. Previous works on self-training acknowledge the importance of unlabeled data but have not delved into their efficient utilization, nor have they paid attention to the problem of high time consumption caused by iterative learning. This paper proposes Incremental Self-training (IST) for semi-supervised learning to fill these gaps. Unlike ST, which processes all data indiscriminately, IST processes data in batches and priority assigns pseudo-labels to unlabeled samples with high certainty. Then, it processes the data around the decision boundary after the model is stabilized, enhancing classifier performance. Our IST is simple yet effective and fits existing self-training-based semi-supervised learning methods. We verify the proposed IST on five datasets and two types of backbone, effectively improving the recognition accuracy and learning speed. Significantly, it outperforms state-of-the-art competitors on three challenging image classification tasks.

HandDiff: 3D Hand Pose Estimation with Diffusion on Image-Point Cloud

Extracting keypoint locations from input hand frames, known as 3D hand pose estimation, is a critical task in various human-computer interaction applications. Essentially, the 3D hand pose estimation can be regarded as a 3D point subset generative problem conditioned on input frames. Thanks to the recent significant progress on diffusion-based generative models, hand pose estimation can also benefit from the diffusion model to estimate keypoint locations with high quality. However, directly deploying the existing diffusion models to solve hand pose estimation is non-trivial, since they cannot achieve the complex permutation mapping and precise localization. Based on this motivation, this paper proposes HandDiff, a diffusion-based hand pose estimation model that iteratively denoises accurate hand pose conditioned on hand-shaped image-point clouds. In order to recover keypoint permutation and accurate location, we further introduce joint-wise condition and local detail condition. Experimental results demonstrate that the proposed HandDiff significantly outperforms the existing approaches on four challenging hand pose benchmark datasets. Codes and pre-trained models are publicly available at https://github.com/cwc1260/HandDiff.

Reflectance Estimation for Proximity Sensing by Vision-Language Models: Utilizing Distributional Semantics for Low-Level Cognition in Robotics

Large language models (LLMs) and vision-language models (VLMs) have been increasingly used in robotics for high-level cognition, but their use for low-level cognition, such as interpreting sensor information, remains underexplored. In robotic grasping, estimating the reflectance of objects is crucial for successful grasping, as it significantly impacts the distance measured by proximity sensors. We investigate whether LLMs can estimate reflectance from object names alone, leveraging the embedded human knowledge in distributional semantics, and if the latent structure of language in VLMs positively affects image-based reflectance estimation. In this paper, we verify that 1) LLMs such as GPT-3.5 and GPT-4 can estimate an object's reflectance using only text as input; and 2) VLMs such as CLIP can increase their generalization capabilities in reflectance estimation from images. Our experiments show that GPT-4 can estimate an object's reflectance using only text input with a mean error of 14.7%, lower than the image-only ResNet. Moreover, CLIP achieved the lowest mean error of 11.8%, while GPT-3.5 obtained a competitive 19.9% compared to ResNet's 17.8%. These results suggest that the distributional semantics in LLMs and VLMs increases their generalization capabilities, and the knowledge acquired by VLMs benefits from the latent structure of language.