arXiv subjects

Ruocheng Wu

Publications and source records attributed to Ruocheng Wu.

6 recordsLinked to original sources

arXiv2026

MeshRepair: Autoregressive Topology Repair for Generated Artisan Meshes

Recent artisan mesh generation methods produce meshes whose topology comes close to the preferences of human artists. But gaps to artist quality remain because generated meshes commonly contain holes, stray faces, and duplicated faces. Regenerating a mesh from scratch is costly, and a new draw may change the preferred topology entirely or introduce errors elsewhere. In this work we study artisan topology correction. The task is to correct the topology of a generated mesh (the draft) in place, toward the layout an artist would make. We propose a method for this task. It is a face-level correction network based on a prefix-LM. It reads the mesh bidirectionally and emits an edit sequence autoregressively. The sequence consists of a deletion phase followed by an addition phase. We propose the novel formulation of the deletion head which picks faces to delete autoregressively and the addition head that selects vertices to form a face to add. The addition head also adds vertices when needed. Untouched faces pass through unchanged, so the corrected mesh keeps the vertices and the layout of the draft. The draft enters as a prefix and only the edits are generated. A correction therefore takes far fewer decoding steps than generating every face again with an autoregressive mesh generator. We benchmark our method against repair tools on scripted defects and on held-out drafts from two mesh generators. Across both scripted defects and generated drafts, our method improves the quad-equivalent face F1, removes non-manifold faces, and cleans up the topology.

cs.GR↗

arXiv2026

MeshCarve: Artisan Mesh Generation with Flow Matching in Compact Latent Spaces

Prior artisan mesh generation works largely predict face tokens autoregressively, which makes inference slow. Recent methods instead flow match continuous latents built by Variational AutoEncoders (VAEs), but reconstruction quality drops significantly when geometry and topology are jointly encoded, and further when the latent space is compressed. We present MeshCarve, a flow matching method that generates entirely in compact latent spaces, generating vertex positions and edge connections separately and sidestepping the difficulty of a joint compact latent. To shorten the token sequence, we propose a hierarchical sparse transformer backbone, instantiated as VertexVAE and EdgeVAE. Instead of encoding fields over the surface voxels, both VAEs anchor on discrete vertices in their latent spaces, which drastically reduces the token sequence length, and our spatial-aware compression shortens it further without costing reconstruction. VertexVAE directly encodes vertex occupancy. For connectivity, we propose vertex-link encoding, which turns arbitrary connectivity between vertices into fixed-length continuous per-vertex embeddings and recovers complex artistic topology faithfully. MeshCarve combines these VAEs with an anchor generator and flow matches on the shortened token sequences. It shows advantages over state-of-the-art autoregressive and flow matching methods on Objaverse and generalizes to Toys4K. To the best of our knowledge, it is among the first artisan mesh generation methods whose every generative stage runs in a spatially compressed latent, with a token sequence only a fraction of the most compressed previous autoregressive and flow matching works.

cs.CV↗

arXiv2026

Agogic: Performance-Timed Music Tokens for LLM-Native Text-to-Symbolic-Music Generation

Text-to-music language models begin with a choice usually made by default: how to tokenize music. Normally entangled with backbone, data, and recipe, its effect has never been measured in isolation. We fix pretrained Qwen3.5 (0.8B-27B), data, budget, and decoding, and swap only the representation across seven tokenizations, anchoring texture metrics to each representation's model-free ceiling. The ordering is clean and surprising: representation, not model size, is the binding variable for distributional fidelity. Scaling the backbone 34x barely moves Frechet Music Distance (FMD), whereas switching representation halves it. PMT, a performance-resolution stream we release (10 ms timing, per-note velocity, multi-track texture; 609 symbols), reaches FMD 159 at 0.8B against 272-286 for beat grids (1.7-1.8x lower, up to 2.8x elsewhere; non-overlapping bootstrap CIs), so a 0.8B performance-resolution model beats a 27B beat grid. It reappears on a 26M from-scratch backbone and a second performance-resolution tokenizer: a property of the class, not one lucky vocabulary. Nor is it a finer-lattice artifact: snapping PMT's onsets to the beat grids' resolution still leaves it 67-129 FMD ahead of both (n=500). The effect is distributional; whether it is audible is a separate question, left open by our probe, with a human study pre-registered. Native caption adherence is weak but separable: a lightweight decode-time constraint doubles instrument-F1 (.28 to .60) and Correct-Key (.16 to .35) at no distributional cost. We release the harness, 25+ checkpoints, two corpora (86.6k aligned across caption/MIDI/ABC/audio; 6.25M captioned, the largest for music), and an imprinting diagnostic: published text-to-MIDI systems reproduce their training distribution near-invariant to the caption (72% vs. 71% chord-time on disjoint domains). The field's next representation claim can now be measured, not asserted.

cs.SD↗

arXiv2025

Dynamic Asset Pricing with α-MEU Model

We study a dynamic asset pricing problem in which a representative agent is ambiguous about the aggregate endowment growth rate and trades a risky stock, human capital, and a risk-free asset to maximize her preference value of consumption represented by the α-maxmin expected utility model. This preference model is known to be dynamically inconsistent, so we consider intra-personal equilibrium strategies for the representative agent and define the market equilibrium as the one in which the strategy that clears the market is an intra-personal equilibrium. We prove the existence and uniqueness of the market equilibrium and show that the asset prices in the equilibrium are the same as in the case when the agent does not perceive any ambiguity but believes in a particular probabilistic model of the endowment process. We show that with reasonable parameter values, the more ambiguity the agent perceives or the more ambiguity-averse she is, the lower the risk-free rate, the higher the stock price, the higher the stock risk premium, and the lower the stock volatility.

q-fin.PR↗

arXiv2025

RealisticDreamer: Guidance Score Distillation for Few-shot Gaussian Splatting

3D Gaussian Splatting (3DGS) has recently gained great attention in the 3D scene representation for its high-quality real-time rendering capabilities. However, when the input comprises sparse training views, 3DGS is prone to overfitting, primarily due to the lack of intermediate-view supervision. Inspired by the recent success of Video Diffusion Models (VDM), we propose a framework called Guidance Score Distillation (GSD) to extract the rich multi-view consistency priors from pretrained VDMs. Building on the insights from Score Distillation Sampling (SDS), GSD supervises rendered images from multiple neighboring views, guiding the Gaussian splatting representation towards the generative direction of VDM. However, the generative direction often involves object motion and random camera trajectories, making it challenging for direct supervision in the optimization process. To address this problem, we introduce an unified guidance form to correct the noise prediction result of VDM. Specifically, we incorporate both a depth warp guidance based on real depth maps and a guidance based on semantic image features, ensuring that the score update direction from VDM aligns with the correct camera pose and accurate geometry. Experimental results show that our method outperforms existing approaches across multiple datasets.

cs.CV↗

arXiv2022

Convergence of Simulated Annealing Using Kinetic Langevin Dynamics

We study the simulated annealing algorithm based on the kinetic Langevin dynamics, in order to find the global minimum of a non-convex potential function. For both the continuous time formulation and a discrete time analogue, we obtain the convergence rate results under technical conditions on the potential function, together with an appropriate choice of the cooling schedule and the time discretization parameters.

math.PR↗