Vectorized High-Definition (HD) map construction requires predictions of the category and point coordinates of map elements (e.g. road boundary, lane divider, pedestrian crossing, etc.). State-of-the-art methods are mainly based on point-level representation learning for regressing accurate point coordinates. However, this pipeline has limitations in obtaining element-level information and handling element-level failures, e.g. erroneous element shape or entanglement between elements. To tackle the above issues, we propose a simple yet effective HybrId framework named HIMap to sufficiently learn and interact both point-level and element-level information. Concretely, we introduce a hybrid representation called HIQuery to represent all map elements, and propose a point-element interactor to interactively extract and encode the hybrid information of elements, e.g. point position and element shape, into the HIQuery. Additionally, we present a point-element con-sistency constraint to enhance the consistency between the point-level and element-level information. Finally, the output point-element integrated HIQuery can be directly converted into map elements' class, point coordinates, and mask. We conduct extensive experiments and consistently outperform previous methods on both nuScenes and Argo-verse2 datasets. Notably, our method achieves 77.8 mAP on the nuScenes dataset, remarkably superior to previous SOTAs by 8.3 mAP at least.

HD map construction is a fundamental and challenging task in autonomous driving to understand the surrounding environment. Recently, Camera-LiDAR BEV feature fusion methods have attracted increasing attention in HD map construction task, which can significantly boost the benchmark. However, existing fusion methods ignore modal interaction and utilize very simple fusion strategy, which suffers from the problems of misalignment and information loss. To tackle this, we propose a novel Multi-modal BEV feature fusion method named MBFusion. Specifically, to solve the semantic misalignment problem between Camera and LiDAR features, we design Cross-modal Interaction Transform (CIT) module to make these two feature spaces interact knowledge with each other to enhance the feature representation by the cross-attention mechanism. Then, we propose a Dual Dynamic Fusion (DDF) module to automatically select valuable information from different modalities for better feature fusion. Moreover, MBFusion is simple, and can be plug-and-played into existing pipelines. We evaluate MBFusion on three architectures, including HDMapNet, VectorMapNet, and MapTR, to show its versatility and effectiveness. Compared with the state-of-the-art methods, MBFusion achieves 3.6% and 4.1% absolute improvements on mAP on the nuScenes and the Argoverse2 datasets, respectively, demonstrating the superiority of our method.

Driving systems often rely on high-definition (HD) maps for precise environmental information, which is crucial for planning and navigation. While current HD map constructors perform well under ideal conditions, their resilience to real-world challenges, \eg, adverse weather and sensor failures, is not well understood, raising safety concerns. This work introduces MapBench, the first comprehensive benchmark designed to evaluate the robustness of HD map construction methods against various sensor corruptions. Our benchmark encompasses a total of 29 types of corruptions that occur from cameras and LiDAR sensors. Extensive evaluations across 31 HD map constructors reveal significant performance degradation of existing methods under adverse weather conditions and sensor failures, underscoring critical safety concerns. We identify effective strategies for enhancing robustness, including innovative approaches that leverage multi-modal fusion, advanced data augmentation, and architectural techniques. These insights provide a pathway for developing more reliable HD map construction methods, which are essential for the advancement of autonomous driving technology. The benchmark toolkit and affiliated code and model checkpoints have been made publicly accessible.

Depth-aware Video Panoptic Segmentation (DVPS) is a challenging task that requires predicting the semantic class and 3D depth of each pixel in a video, while also segmenting and consistently tracking objects across frames. Predominant methodologies treat this as a multi-task learning problem, tackling each constituent task independently, thus restricting their capacity to leverage interrelationships amongst tasks and requiring parameter tuning for each task. To surmount these constraints, we present Slot-IVPS, a new approach employing an object-centric model to acquire unified object representations, thereby facilitating the model's ability to simultaneously capture semantic and depth information. Specifically, we introduce a novel representation, Integrated Panoptic Slots (IPS), to capture both semantic and depth information for all panoptic objects within a video, encompassing background semantics and foreground instances. Subsequently, we propose an integrated feature generator and enhancer to extract depth-aware features, alongside the Integrated Video Panoptic Retriever (IVPR), which iteratively retrieves spatial-temporal coherent object features and encodes them into IPS. The resulting IPS can be effortlessly decoded into an array of video outputs, including depth maps, classifications, masks, and object instance IDs. We undertake comprehensive analyses across four datasets, attaining state-of-the-art performance in both Depth-aware Video Panoptic Segmentation and Video Panoptic Segmentation tasks.

This paper proposes RefXVC, a method for cross-lingual voice conversion (XVC) that leverages reference information to improve conversion performance. Previous XVC works generally take an average speaker embedding to condition the speaker identity, which does not account for the changing timbre of speech that occurs with different pronunciations. To address this, our method uses both global and local speaker embeddings to capture the timbre changes during speech conversion. Additionally, we observed a connection between timbre and pronunciation in different languages and utilized this by incorporating a timbre encoder and a pronunciation matching network into our model. Furthermore, we found that the variation in tones is not adequately reflected in a sentence, and therefore, we used multiple references to better capture the range of a speaker's voice. The proposed method outperformed existing systems in terms of both speech quality and speaker similarity, highlighting the effectiveness of leveraging reference information in cross-lingual voice conversion.

This paper presents an accented text-to-speech (TTS) synthesis framework with limited training data. We study two aspects concerning accent rendering: phonetic (phoneme difference) and prosodic (pitch pattern and phoneme duration) variations. The proposed accented TTS framework consists of two models: an accented front-end for grapheme-to-phoneme (G2P) conversion and an accented acoustic model with integrated pitch and duration predictors for phoneme-to-Mel-spectrogram prediction. The accented front-end directly models the phonetic variation, while the accented acoustic model explicitly controls the prosodic variation. Specifically, both models are first pretrained on a large amount of data, then only the accent-related layers are fine-tuned on a limited amount of data for the target accent. In the experiments, speech data of three English accents, i.e., General American English, Irish English, and British English Received Pronunciation, are used for pre-training. The pretrained models are then fine-tuned with Scottish and General Australian English accents, respectively. Both objective and subjective evaluation results show that the accented TTS frontend fine-tuned with a small accented phonetic lexicon (5k words) effectively handles the phonetic variation of accents, while the accented TTS acoustic model fine-tuned with a limited amount of accented speech data (approximately 3 minutes) effectively improves the prosodic rendering including pitch and duration. The overall accent modeling contributes to improved speech quality and accent similarity.

Accent Conversion (AC) seeks to change the accent of speech from one (source) to another (target) while preserving the speech content and speaker identity. However, many existing AC approaches rely on source-target parallel speech data during training or reference speech at run-time. We propose a novel accent conversion framework without the need for either parallel data or reference speech. Specifically, a text-to-speech (TTS) system is first pretrained with target-accented speech data. This TTS model and its hidden representations are expected to be associated only with the target accent. Then, a speech encoder is trained to convert the accent of the speech under the supervision of the pretrained TTS model. In doing so, the source-accented speech and its corresponding transcription are forwarded to the speech encoder and the pretrained TTS, respectively. The output of the speech encoder is optimized to be the same as the text embedding in the TTS system. At run-time, the speech encoder is combined with the pretrained speech decoder to convert the source-accented speech toward the target. In the experiments, we converted English with two source accents (Chinese/Indian) to the target accent (American/British/Canadian). Both objective metrics and subjective listening tests successfully validate that the proposed approach generates speech samples that are close to the target accent with high speech quality.

Cross-lingual voice conversion (XVC) transforms the speaker identity of a source speaker to that of a target speaker who speaks a different language. Due to the intrinsic differences between languages, the converted speech may carry an unwanted foreign accent. In this paper, we first investigate the intelligibility of the converted speech and confirm the performance degradation caused by the accent/intelligibility issue. With the goal of generating native-sounding speech, this paper further proposes a novel training scheme with two additional linguistic losses for speech waveform generation: 1) a frame-wise phonetic content loss derived from bottleneck features, and 2) an automatic speech recognition loss on characters. Experiments were conducted between English and Mandarin Chinese conversions. The experimental results confirmed that the generated speech sounds more natural with the proposed linguistic losses and the proposed solution significantly improves speech intelligibility.

Video Panoptic Segmentation (VPS) aims at assigning a class label to each pixel, uniquely segmenting and identifying all object instances consistently across all frames. Classic solutions usually decompose the VPS task into several subtasks and utilize multiple surrogates (e.g. boxes and masks, centers and offsets) to represent objects. However, this divide-and-conquer strategy requires complex post-processing in both spatial and temporal domains and is vulnerable to failures from surrogate tasks. In this paper, inspired by object-centric learning which learns compact and robust object representations, we present Slot- VPS, the first end-to-end framework for this task. We encode all panoptic entities in a video, including both foreground instances and background semantics, with a unified representation called panoptic slots. The coherent spatio-temporal object's information is retrieved and encoded into the panoptic slots by the proposed Video Panoptic Retriever, enabling to localize, segment, differentiate, and associate objects in a unified manner. Finally, the output panoptic slots can be directly converted into the class, mask, and object ID of panoptic objects in the video. We conduct extensive ablation studies and demonstrate the effectiveness of our approach on two benchmark datasets, Cityscapes- VP S (val and test sets) and VIPER (val set), achieving new state-of-the-art performance of 63.7, 63.3 and 56.2 VPQ, respectively.

We present a novel voice conversion (VC) framework by learning from a text-to-speech (TTS) synthesis system, that is called TTS-VC transfer learning or TTL-VC for short. We first develop a multi-speaker speech synthesis system with sequence-to-sequence encoder-decoder architecture, where the encoder extracts the linguistic representations of input text, while the decoder, conditioned on target speaker embedding, takes the context vectors and the attention recurrent network cell output to generate target acoustic features. We take advantage of the fact that TTS system maps input text to speaker independent context vectors, thus re-purpose such a mapping to supervise the training of the latent representations of an encoder-decoder voice conversion system. In the voice conversion system, the encoder takes speech instead of text as the input, while the decoder is functionally similar to the TTS decoder. As we condition the decoder on a speaker embedding, the system can be trained on non-parallel data for any-to-any voice conversion. During voice conversion training, we present both text and speech to speech synthesis and voice conversion networks respectively. At run-time, the voice conversion network uses its own encoder-decoder architecture without the need of text input. Experiments show that the proposed TTL-VC system outperforms two competitive voice conversion baselines consistently, namely phonetic posteriorgram and AutoVC methods, in terms of speech quality, naturalness, and speaker similarity.

Cross-lingual personalized speech generation seeks to synthesize a target speakers voice from only a few training samples that are in a different language. One popular technique is to condition a speech synthesizer on a speaker embedding, that characterizes the target speaker. Unfortunately, such a speaker embedding is usually affected by the language being spoken, which compromises the speaker similarity in cross-lingual personalized speech generation. In this paper, we propose a novel speaker encoding mechanism that learns a language agnostic speaker embedding to characterize speaker individuality. Specifically, we adopt an encoder-decoder architecture to disentangle the language information from speaker embeddings via multi-task learning. We conduct experiments on both voice conversion and text-to-speech synthesis between English and Mandarin that involve cross-lingual speech generation. All objective and subjective evaluations consistently confirm that the proposed speaker embedding is language agnostic, thus improving cross-lingual personalized speech generation in terms of speaker similarity.

Spoken languages are similar phonetically because humans have a common vocal production system. However, each language has a unique phonetic repertoire and phonotactic rule. In cross-lingual voice conversion, source speaker and target speaker speak different languages. The challenge is how to project the speaker identity of the source speaker to that of the target across two different phonetic systems. A typical voice conversion system employs a generator-vocoder pipeline, where the generator is responsible for conversion, and the vocoder is for waveform reconstruction. We propose a novel Multi-Task WaveRNN with an integrated architecture for cross-lingual voice conversion. The WaveRNN is trained on two sets of monolingual data via a two-task learning. The integrated architecture takes linguistic features as input and outputs speech waveform directly. Voice conversion experiments are conducted between English and Mandarin, which confirm the effectiveness of the proposed method in terms of speech quality and speaker similarity.

This paper presents a cross-lingual voice conversion framework that adopts a modularized neural network. The modularized neural network has a common input structure that is shared for both languages, and two separate output modules, one for each language. The idea is motivated by the fact that phonetic systems of languages are similar because humans share a common vocal production system, but acoustic renderings, such as prosody and phonotactic, vary a lot from language to language. The modularized neural network is trained to map Phonetic PosteriorGram (PPG) to acoustic features for multiple speakers. It is conditioned on a speaker i-vector to generate the desired target voice. We validated the idea between English and Mandarin languages in objective and subjective tests. In addition, mixed-lingual PPG derived from a unified English-Mandarin acoustic model is proposed to capture the linguistic information from both languages. It is found that our proposed modularized neural network significantly outperforms the baseline approaches in terms of speech quality and speaker individuality, and mixed-lingual PPG representation further improves the conversion performance.

This paper presents a cross-lingual voice conversion approach using bilingual Phonetic PosteriorGram (PPG) and average modeling. The proposed approach makes use of bilingual PPGs to represent speaker-independent features of speech signals from different languages in the same feature space. In particular, a bilingual PPG is formed by stacking two monolingual PPG vectors, which are extracted from two monolingual speech recognition systems. The conversion model is trained to learn the relationship between bilingual PPGs and the corresponding acoustic features. To leverage the linguistic and acoustic information from other speakers in different languages, an average model is trained with multiple speakers in both source and target languages. I-vector is utilized as an additional input feature of the average model for network adaptation. Experiments are performed for intralingual and cross-lingual voice conversion between English and Mandarin speakers. Both objective and subjective evaluations demonstrate the effectiveness of our proposed approach.

For multi-view face alignment, we have to deal with two major problems: 1) the problem of multi-modality caused by diverse shape variation when the view changes dramatically; 2) the varying number of feature points caused by self-occlusion. Previous works have used nonlinear models or view based methods for multi-view face alignment. However, they either assume all feature points are visible or apply a set of discrete models separately without a uniform criterion. In this paper, we propose a unified framework to solve the problem of multi-view face alignment, in which, both the multi-modality and variable feature points are modeled by a Bayesian mixture model. We first develop a mixture model to describe the shape distribution and the feature point visibility, and then use an efficient EM algorithm to estimate the model parameters and the regularized shape. We use a set of experiments on several datasets to demonstrate the improvement of our method over traditional methods.

A new approach for localizing facial structure in videos is proposed in this paper by modeling shape alignment dynamically. The approach makes use of the spatial-temporal continuity of videos and incorporates it into a statistical shape model which is called constrained Bayesian tangent shape model (C-BTSM). Our model includes a prior 2D shape model learnt from labeled examples, an observation model obtained from observation in the current input image, and a constraint model derived from the prediction by the previous frames. By modeling the prior, observation and constraint in a probabilistic framework, the task of aligning shape in each frame of a video is performed as a procedure of MAP parameter estimation, in which the pose and shape parameters are recovered simultaneously. Experiments on low quality videos from web cameras are provided to demonstrate the robustness and accuracy of our algorithm.