Stories and emotions in movies emerge through the effect of well-thought-out
directing decisions, in particular camera placement and movement over time.
Crafting compelling camera trajectories remains a complex iterative process,
even for skilful artists. To tackle this, in this paper, we propose a dataset
called the Exceptional Trajectories (E.T.) with camera trajectories along with
character information and textual captions encompassing descriptions of both
camera and character. To our knowledge, this is the first dataset of its kind.
To show the potential applications of the E.T. dataset, we propose a
diffusion-based approach, named DIRECTOR, which generates complex camera
trajectories from textual captions that describe the relation and
synchronisation between the camera and characters. To ensure robust and
accurate evaluations, we train on the E.T. dataset CLaTr, a Contrastive
Language-Trajectory embedding for evaluation metrics. We posit that our
proposed dataset and method significantly advance the democratization of
cinematography, making it more accessible to common users.

通过精心考虑的导演决策，特别是相机位置和随时间变化的运动，电影中的故事和情感呈现。为了解决相机轨迹建立的复杂过程，我们提出了一个数据集 Exceptional Trajectories（E.T.），其中包括相机轨迹、字符信息和文字标题，涵盖了相机和字符的描述。我们提出了一种基于扩散的方法 DIRECTOR，该方法根据描述相机与字符之间关系和同步的文本标题生成复杂的相机轨迹，以展示 E.T. 数据集的潜在应用。在 E.T. 数据集上进行训练的 CLaTr 对比学习方法用于评估指标的稳健性和准确性。我们认为我们提出的数据集和方法显著推动了电影摄影术的民主化进程，使其对普通用户更易接触。

E.T. 异常轨迹：具有角色意识的文本到相机轨迹生成

E.T. the Exceptional Trajectories: Text-to-camera-trajectory generation  with character awareness

We present a Character-Word Long Short-Term Memory Language Model which both
reduces the perplexity with respect to a baseline word-level language model and
reduces the number of parameters of the model. Character information can reveal
structural (dis)similarities between words and can even be used when a word is
out-of-vocabulary, thus improving the modeling of infrequent and unknown words.
By concatenating word and character embeddings, we achieve up to 2.77% relative
improvement on English compared to a baseline model with a similar amount of
parameters and 4.57% on Dutch. Moreover, we also outperform baseline word-level
models with a larger number of parameters.

使用长期短期记忆（LSTM）、字符信息和单词嵌入等技术，设计了一种基于字符的语言模型，有效提高了对单词的建模能力，优于传统基于单词的模型。