A 3D burr puzzle is a 3D model that consists of interlocking pieces with a single-key property. That is, when the puzzle is assembled, all the pieces are notched except one single key component which remains mobile. The intriguing property of the assembled burr puzzle is that it is stable, perfectly interlocked, without glue or screws, etc. Moreover, a burr puzzle consisting of a small number of pieces is still rather difficult to solve since the assembly must follow certain orders while the combinatorial complexity of the puzzle's piece arrangements is extremely high. In this paper, we generalize the 6-piece orthogonal burr puzzle (a knot) to design and model burr puzzles from 3D models. Given a 3D input model, we first interactively embed a network of knots into the 3D shape. Our method automatically optimizes and arranges the orientation of each knot, and modifies pieces of adjacent knots with an appropriate connection type. Then, following the geometry of the embedded pieces, the entire 3D model is partitioned by splitting the solid while respecting the assembly motion of embedded pieces. The main technical challenge is to enforce the single-key property and ensure the assembly/disassembly remains feasible, as the puzzle pieces in a network of knots are highly interlocked. Lastly, we also present an automated approach to generate the visualizations of the puzzle assembly process.

A 3D burr puzzle is a 3D model that consists of interlocking pieces with a single-key property. That is, when the puzzle is assembled, all the pieces are notched except one single key component which remains mobile. The intriguing property of the assembled burr puzzle is that it is stable, perfectly interlocked, without glue or screws, etc. Moreover, a burr puzzle consisting of a small number of pieces is still rather difficult to solve since the assembly must follow certain orders while the combinatorial complexity of the puzzle's piece arrangements is extremely high. In this paper, we generalize the 6-piece orthogonal burr puzzle (a knot) to design and model burr puzzles from 3D models. Given a 3D input model, we first interactively embed a network of knots into the 3D shape. Our method automatically optimizes and arranges the orientation of each knot, and modifies pieces of adjacent knots with an appropriate connection type. Then, following the geometry of the embedded pieces, the entire 3D model is partitioned by splitting the solid while respecting the assembly motion of embedded pieces. The main technical challenge is to enforce the single-key property and ensure the assembly/disassembly remains feasible, as the puzzle pieces in a network of knots are highly interlocked. Lastly, we also present an automated approach to generate the visualizations of the puzzle assembly process.

Previous research on impossible figures focuses extensively on single view modeling and rendering. Existing computer games that employ impossible figures as navigation maze for gaming either use a fixed third-person view with axonometric projection to retain the figure's impossibility perception, or simply break the figure's impossibility upon view changes. In this paper, we present a new approach towards 3D gaming with impossible figures, delivering for the first time navigation in 3D mazes constructed from impossible figures. Such result cannot be achieved by previous research work in modeling impossible figures. To deliver seamless gaming navigation and interaction, we propose i) a set of guiding principles for bringing out subtle perceptions and ii) a novel computational approach to construct 3D structures from impossible figure images and then to dynamically construct the impossible-figure maze subjected to user's view. In the end, we demonstrate and discuss our method with a variety of generic maze types.

Example-based image relighting aims to relight an input image to follow the lighting settings of another target example image. Deep learning-based methods for such tasks have become highly popular. However, they are often limited by the geometric priors or suffer from shadow reconstruction and lack of texture details. In this paper, we propose an image-to-image translation network called DGATRN to tackle this problem by enhancing feature extraction and unveiling context information to achieve visually plausible example-based image relighting. Specifically, the proposed DGATRN consists of a scene extraction, a shadow calibration, and a rendering network, and our key contribution lies in the first two networks. We propose an up- and downsampling approach to improve the feature extraction capability to capture scene and texture details better. We also introduce a feature attention downsampling block and a knowledge transfer to utilize the attention impact and underlying knowledge connection between scene and shadow. Experiments were conducted to evaluate the usefulness and effectiveness of the proposed method.

This paper endeavors to investigate how we can understand creativity concerning computation by rearticulating imagination in artistic practices. Drawing on Yuk Hui’s concept of artificial imagination and Bernard Stiegler’s thinking on organology the paper uses the Choi Hung Estate project as a case study demonstrating the potential of Gaussian splatting as a supplement for the imagination in producing dynamic, interactive digital artworks that open the path for conceiving collective memories. By thinking of creativity and beyond computation, the paper unpacks the possibilities for re-grounding the philosophical foundation of Gaussian splatting from an organological perspective.