Audio-to-motion generation is an important task with applications in virtual avatar creation for XR systems and intelligent robot control in daily life scenarios. However, most existing motion generation methods rely on a single encoder-decoder architecture to model all body parts simultaneously, which limits their ability to capture the diverse and complex motions exhibited by humans. In this paper, we propose a novel method, SpeechCAT, that employs three separate encoder-decoder modules to individually model the motions of the face, body, and hands. To capture the relationships and synchronization among these body parts, we introduce a cross-attention mechanism to effectively learn their correlations. SpeechCAT ensures sufficient capacity to model the unique characteristics of each body part while preserving the coherence between them. Our experimental results demonstrate the superiority of SpeechCAT over baseline methods, highlighting its effectiveness in generating diverse, realistic, and synchronized motions with face, body, and hand parts. ... Read more

We propose GazeNeRF, a 3D-aware method for the task of gaze redirection. Existing gaze redirection methods operate on 2D images and struggle to generate 3D consistent results. Instead, we build on the intuition that the face region and eyeballs are separate 3D structures that move in a coordinated yet independent fashion. Our method leverages recent advancements in conditional image-based neural radiance fields and proposes a two-stream architecture that predicts volumetric features for the face and eye regions separately. Rigidly transforming the eye features via a 3D rotation matrix provides fine-grained control over the desired gaze angle. The final, redirected image is then attained via differentiable volume compositing. Our experiments show that this architecture outperforms naively conditioned NeRF baselines as well as previous state-of-the-art 2D gaze redirection methods in terms of redirection accuracy and identity preservation. Code and models will be released for research purposes. ... Read more

Understanding how cities visually differ from each others is interesting for planners, residents, and historians. We investigate the interpretation of deep features learned by convolutional neural networks (CNNs) for city recognition. Given a trained city recognition network, we first generate weighted masks using the known Grad-CAM technique and to select the most discriminate regions in the image. Since the image classification label is the city name, it contains no information of objects that are class-discriminate, we investigate the interpretability of deep representations with two methods. (i) Unsupervised method is used to cluster the objects appearing in the visual explanations. (ii) A pretrained semantic segmentation model is used to label objects in pixel level, and then we introduce statistical measures to quantitatively evaluate the interpretability of discriminate objects. The influence of network architectures and random initializations in training, is studied on the interpretability of CNN features for city recognition. The results suggest that network architectures would affect the interpretability of learned visual representations greater than different initializations. ... Read more

We address the interpretability of convolutional neural networks (CNNs) for predicting a geo-location from an image. In a pilot experiment we classify images of Pittsburgh vs Tokyo and visualize the learned CNN filters. We found that varying the CNN architecture leads to variating in the visualized filters. This calls for further investigation of the effective parameters on the interpretability of CNNs. ... Read more