Recently, Image-to-Music (I2M) generation has garnered significant attention, with potential applications in fields such as gaming, advertising, and multi-modal art creation. However, due to the ambiguous and subjective nature of I2M tasks, most end-to-end methods lack interpretability, leaving users puzzled about the generation results. Even methods based on emotion mapping face controversy, as emotion represents only a singular aspect of art. Additionally, most learning-based methods require substantial computational resources and large datasets for training, hindering accessibility for common users. To address these challenges, we propose the first Vision Language Model (VLM)-based I2M framework that offers high interpretability and low computational cost. Specifically, we utilize ABC notation to bridge the text and music modalities, enabling the VLM to generate music using natural language. We then apply multi-modal Retrieval-Augmented Generation (RAG) and self-refinement techniques to allow the VLM to produce high-quality music without external training. Furthermore, we leverage the generated motivations in text and the attention maps from the VLM to provide explanations for the generated results in both text and image modalities. To validate our method, we conduct both human studies and machine evaluations, where our method outperforms others in terms of music quality and music-image consistency, indicating promising results. Our code is available at https://github.com/RS2002/Image2Music.

A common approach for Augmented Reality labeling is to display the label text on a flag planted into the real world element at a 3D anchor point. When there are more than just a few labels, the efficiency of the interface decreases as the user has to search for a given label sequentially. The search can be accelerated by sorting the labels alphabetically, but sorting all labels results in long and intersecting leader lines from the anchor points to the labels. This paper proposes a partially-sorted concentric label layout that leverages the search efficiency of sorting while avoiding the label display problems of long or intersecting leader lines. The labels are partitioned into a small number of sorted sequences displayed on circles of increasing radii. Since the labels on a circle are sorted, the user can quickly search each circle. A tight upper bound derived from circular permutation theory limits the number of circles and thereby the complexity of the label layout. For example, 12 labels require at most three circles. When the application allows it, the labels are presorted to further reduce the number of circles in the layout. The layout was tested in a user study where it significantly reduced the label searching time compared to a conventional single-circle layout.

In a co-located multi-user collaborative virtual reality (VR) application a collaborator should be able to indicate a workspace location to the user, such that they can refer to it simultaneously as they work together. Due to their different viewpoints, the collaborator sees some parts of the virtual environment (VE) that the user does not, and communication breaks down when the collaborator's reference is not visible to the user. The conventional solutions of asking the user to move around to gain line of sight to the collaborator's reference, or of asking the user to toggle back and forth between their view and that of the collaborator can be inefficient and ineffective. This paper proposes a method for improving collaboration in VR by alleviating the disparity between the user and the collaborator views of the VE. The user is shown a multiperspective visualization of the VE that transitions smoothly from the user to the collaborator's perspective. The multiperpsective visualization is based on the switch camera, a novel camera model with curved rays that splice together the user and collaborator views. The multiperspective visualization is computed first by warping the VE geometry, through projection with the switch camera followed by unprojection with a conventional camera, and then by rendering the warped VE conventionally, for each user eye. A controlled user study with three tasks shows that VR collaboration using the switch camera multiperpsective visualization is faster, more reliable, and less taxing on the user than the conventional approaches of viewpoint translation or view toggling.