We present NRGMark, a region-based image watermarking framework to embed provenance metadata into composite graphic designs such as posters. NRGMark enables imperceptible watermarking of distinct visual elements each carrying independent metadata on aspects like environmental impact, such as the energy consumption associated with generative AI (GenAI) use. NRGMark extends image watermark encoder-decoder models by incorporating an object localization network to detect and decode multiple watermarked regions within a document, even under image transformations and physical print–scan degradation. NRGMark interoperates with several watermarking techniques and the emerging C2PA open standard for media provenance to encode environmental impact metadata. We demonstrate NRGMark on both synthetic and real-world design layouts, illustrating its potential to support energy transparency in the age of GenAI.

Abstract We present a real‐time method to capture and simulate the dynamic behavior of watercolor painting. We develop a physically accurate, grid‐based, real‐time fluid simulation based on a reparameterized Thin Film model. The equations are rewritten so as to create principled parameters that finely control the length, thickness, and frequency of dripping. Our close connection with physics allows both theoretical and experimental validation of our method. The resulting system can reproduce dripping, fluid‐air interface, and pigment advection and diffusion, all controllable by the user in real‐time. Our experiments show that artists can use our system to create interesting and varied digital paintings.

Abstract Rendering tree‐based analytical Signed Distance Fields (SDFs) through sphere tracing often requires to evaluate many primitives per tracing step, for many steps per pixel of the end image. This cost quickly becomes prohibitive as the number of primitives that constitute the SDF grows. In this paper, we alleviate this cost by computing local pruned trees that are equivalent to the full tree within their region of space while being much faster to evaluate. We introduce an efficient hierarchical tree pruning method based on the Lipschitz property of SDFs, which is compatible with hard and smooth CSG operators. We propose a GPU implementation that enables real‐time sphere tracing of complex SDFs composed of thousands of primitives with dynamic animation. Our pruning technique provides significant speedups for SDF evaluation in general, which we demonstrate on sphere tracing tasks but could also lead to significant improvement for SDF discretization or polygonization.

We present closed-form expressions for Green and biharmonic coordinates with respect to polynomial curved 2D cages, enabling reliable cage-based image deformation both to and from a curved cage. We further provide closed-form expressions for first- and second-order derivatives of these coordinates with respect to the encoded position. This enables the use of variational solvers for interacting with the 2D shape at arbitrary points while keeping the fast decoding strength of cage-based deformation, which we illustrate for a variety of elastic deformation energies.

Procedural implicit surfaces are a popular representation for shape modeling. They provide a simple framework for complex geometric operations such as Booleans, blending and deformations. However, their editability remains a challenging task: as the definition of the shape is purely implicit, direct manipulation of the shape cannot be performed. Thus, parameters of the model are often exposed through abstract sliders, which have to be nontrivially created by the user and understood by others for each individual model to modify. Further, each of these sliders needs to be set one by one to achieve the desired appearance. To circumvent this laborious process while preserving editability, we propose to directly manipulate the implicit surface in the viewport. We let the user naturally interact with the output shape, leveraging points on a co-parameterization we design specifically for implicit surfaces, to guide the parameter updates and reach the desired appearance faster. We leverage our automatic differentiation of the procedural implicit surface to propagate interactions made by the user in the viewport to the shape parameters themselves. We further design a solver that uses such information to guide an intuitive and smooth user workflow. We demonstrate different editing processes across multiple implicit shapes and parameters that would be tedious by tuning sliders.

As a natural extension to the harmonic coordinates, the biharmonic coordinates have been found superior for planar shape and image manipulation with an enriched deformation space. However, the 3D biharmonic coordinates and their derivatives have remained unexplored. In this work, we derive closed-form expressions for biharmonic coordinates and their derivatives for 3D triangular cages. The core of our derivation lies in computing the closed-form expressions for the integral of the Euclidean distance over a triangle and its derivatives. The derived 3D biharmonic coordinates not only fill a missing component in methods of generalized barycentric coordinates but also pave the way for various interesting applications in practice, including producing a family of biharmonic deformations, solving variational shape deformations, and even unlocking the closed-form expressions for recently-introduced Somigliana coordinates for both fast and accurate evaluations.

Parametric shapes model objects as programs producing a geometry based on a few semantic degrees of freedom, called hyper-parameters. These shapes are the typical output of non-destructive modeling, CAD modeling or rigging. However they suffer from the core issue of being manipulated only indirectly, through a series of values rather than the geometry itself. In this paper, we introduce an amendment process of the underlying direct acyclic graph (DAG) of a parametric shape. This amendment enables a local differentiation of the shape w.r.t. its hyper-parameters that we leverage to provide interactive direct manipulation of the output. By acting on the shape synthesis process itself, our method is agnostic to the variations of the connectivity and topology that may occur in its output while changing the input hyper-parameters. Furthermore, our method is oblivious to the internal logic of the DAG nodes. We illustrate our approach on a collection of examples combining the typical nodes found in modern parametric modeling packages - such as deformation, booleans and surfacing operators - for which our method provides the user with inverse control over the hyper-parameters through a brush stroke metaphor.

Abstract Dense dynamic aggregates of similar elements are frequent in natural phenomena and challenging to render under full real time constraints. The optimal representation to render them changes drastically depending on the distance at which they are observed, ranging from sets of detailed textured meshes for near views to point clouds for distant ones. Our multiscale representation use impostors to achieve the mid‐range transition from mesh‐based to point‐based scales. To ensure a visual continuum, the impostor model should match as closely as possible the mesh on one side, and reduce to a single pixel response that equals point rendering on the other. In this paper, we propose a model based on rich spherical impostors, able to combine precomputed as well as dynamic procedural data, and offering seamless transitions from close instanced meshes to distant points. Our approach is architectured around an on‐the‐fly discrimination mechanism and intensively exploits the rough spherical geometry of the impostor proxy. In particular, we propose a new sampling mechanism to reconstruct novel views from the precomputed ones, together with a new conservative occlusion culling method, coupled with a two‐pass rendering pipeline leveraging early‐Z rejection. As a result, our system scales well and is even able to render sand, while supporting completely dynamic stackings.