Nowadays, pre-training models on large-scale datasets and fine-tuning models on task-specific datasets have become common paradigms, achieving impressive success in natural language processing and 2D vision. Nonetheless, the potential of this paradigm has not been fully explored in 3D vision due to the scale of the datasets. To overcome this, we propose BoostPoint, a novel pipeline that uses large-scale rendered images as 3D point cloud model inputs for pre-training and uses general 3D tasks for fine-tuning. In BoostPoint, we propose a novel learning-free image-to-point (I2P) module to transform raw pixels into required inputs. Specifically, we view pixels as unorganized points, including essential raw features (e.g., color) and positional information (e.g., coordinates). Employing simple linear iterative clustering (SLIC), the I2P module effectively groups these unorganized points into superpixels, facilitating point cloud backbone pre-training. Furthermore, we employ a modality-agnostic debiasing mechanism during pre-training to prevent negative transfer in downstream tasks. Extensive fine-tuning experiments show that BoostPoint provides significant improvements to 3D point cloud backbones for 3D point cloud classification and part segmentation.

It remains an interesting and challenging problem to synthesize a vivid and realistic singing face driven by music. In this paper, we present a method for this task with natural motions for the lips, facial expression, head pose, and eyes. Due to the coupling of mixed information for the human voice and backing music in common music audio signals, we design a decouple-and-fuse strategy to tackle the challenge. We first decompose the input music audio into a human voice stream and a backing music stream. Due to the implicit and complicated correlation between the two-stream input signals and the dynamics of the facial expressions, head motions, and eye states, we model their relationship with an attention scheme, where the effects of the two streams are fused seamlessly. Furthermore, to improve the expressivenes of the generated results, we decompose head movement generation in terms of speed and direction, and decompose eye state generation into short-term blinking and long-term eye closing, modeling them separately. We have also built a novel dataset, SingingFace, to support training and evaluation of models for this task, including future work on this topic. Extensive experiments and a user study show that our proposed method is capable of synthesizing vivid singing faces, qualitatively and quantitatively better than the prior state-of-the-art.