Reconstructing object deformation from a single image remains a significant challenge in computer vision and graphics. Existing methods typically rely on multi-view video to recover deformation, limiting their applicability under constrained scenarios. To address this, we propose DeformSplat, a novel framework that effectively guides 3D Gaussian deformation from only a single image. Our method introduces two main technical contributions. First, we present Gaussian-to-Pixel Matching which bridges the domain gap between 3D Gaussian representations and 2D pixel observations. This enables robust deformation guidance from sparse visual cues. Second, we propose Rigid Part Segmentation consisting of initialization and refinement. This segmentation explicitly identifies rigid regions, crucial for maintaining geometric coherence during deformation. By combining these two techniques, our approach can reconstruct consistent deformations from a single image. Extensive experiments demonstrate that our approach significantly outperforms existing methods and naturally extends to various applications, such as frame interpolation and interactive object manipulation. Project page : https://vision3d-lab.github.io/deformsplat

Connectionist Temporal Classification (CTC) model is a very efficient method for modeling sequences, especially for speech data.In order to use the CTC model as an Automatic Speech Recognition (ASR) task, the beam search decoding with an external language model like n-gram LM is necessary to obtain reasonable results.In this paper, we analyze the blank label in CTC beam search deeply and propose a very simple method to reduce the amount of calculation resulting in faster beam search decoding speed.With this method, we can get up to 78% faster decoding speed than ordinary beam search decoding with a very small loss of accuracy in LibriSpeech datasets.We prove this method is effective not only practically by experiments but also theoretically by mathematical reasoning.We also observe that this reduction is more obvious if the accuracy of the model is higher. 1

Punctuated text prediction is crucial for automatic speech recognition as it enhances readability and impacts downstream natural language processing tasks.In streaming scenarios, the ability to predict punctuation in real-time is particularly desirable but presents a difficult technical challenge.In this work, we propose a method for predicting punctuated text from input speech using a chunk-based Transformer encoder trained with Connectionist Temporal Classification (CTC) loss.The acoustic model trained with long sequences by concatenating the input and target sequences can learn punctuation marks attached to the end of sentences more effectively.Additionally, by combining CTC losses on the chunks and utterances, we achieved both the improved F1 score of punctuation prediction and Word Error Rate (WER).

Most End-to-End (E2E) Spoken Language Understanding (SLU) networks leverage the pre-trained Automatic Speech Recognition (ASR) networks but still lack the capability to understand the semantics of utterances, crucial for the SLU task. To solve this, recently proposed studies use pre-trained Natural Language Understanding (NLU) networks. However, it is not trivial to fully utilize both pre-trained networks; many solutions were proposed, such as Knowledge Distillation (KD), cross-modal shared embedding and network integration with Interface. We propose a simple and robust integration method for the E2E SLU network with a novel Interface, Continuous Token Interface (CTI). CTI is a junctional representation of the ASR and NLU networks when both networks are pre-trained with the same vocabulary. Thus, we can train our SLU network in an E2E manner without additional modules, such as Gumbel-Softmax. We evaluate our model using SLURP, a challenging SLU dataset and achieve state-of-the-art scores on intent classification and slot filling tasks. We also verify that the NLU network, pre-trained with Masked Language Model (MLM), can utilize a noisy textual representation of CTI. Moreover, we train our model with extra data, SLURP-Synth, and get better results.